From d38736481906b6fb019fa545d14df25a9085a004 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 02:54:25 +1000 Subject: [PATCH 01/20] Created new files and added data loading --- recognition/.gitignore | 1 + recognition/MySolution/.gitignore | 4 + recognition/MySolution/Methods.ipynb | 372 -- recognition/MySolution/dataset.py | 106 + recognition/MySolution/modules.py | 0 recognition/MySolution/predict.py | 0 recognition/MySolution/train.py | 0 recognition/MySolution/unet_main.ipynb | 6323 ------------------------ recognition/MySolution/utils.py | 0 9 files changed, 111 insertions(+), 6695 deletions(-) create mode 100644 recognition/.gitignore create mode 100644 recognition/MySolution/.gitignore delete mode 100644 recognition/MySolution/Methods.ipynb create mode 100644 recognition/MySolution/dataset.py create mode 100644 recognition/MySolution/modules.py create mode 100644 recognition/MySolution/predict.py create mode 100644 recognition/MySolution/train.py delete mode 100644 recognition/MySolution/unet_main.ipynb create mode 100644 recognition/MySolution/utils.py diff --git a/recognition/.gitignore b/recognition/.gitignore new file mode 100644 index 0000000000..5e4988b683 --- /dev/null +++ b/recognition/.gitignore @@ -0,0 +1 @@ +AD_NC/* \ No newline at end of file diff --git a/recognition/MySolution/.gitignore b/recognition/MySolution/.gitignore new file mode 100644 index 0000000000..28dbf12095 --- /dev/null +++ b/recognition/MySolution/.gitignore @@ -0,0 +1,4 @@ +__pycache__/** +AD_NC +*.npy +*.pickle \ No newline at end of file diff --git a/recognition/MySolution/Methods.ipynb b/recognition/MySolution/Methods.ipynb deleted file mode 100644 index c95fb6db2a..0000000000 --- a/recognition/MySolution/Methods.ipynb +++ /dev/null @@ -1,372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "FjZ_XxgC57LQ" - }, - "outputs": [], - "source": [ - "import tensorflow as tf \n", - "import pathlib\n", - "import numpy as np\n", - "from matplotlib import pyplot\n", - "from matplotlib import image\n", - "import glob" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "yPJNfJkC2R08" - }, - "outputs": [], - "source": [ - "def download_oasis ():\n", - " \n", - " #download oasis brain MRI data\n", - " dataset_url = \"https://cloudstor.aarnet.edu.au/plus/s/n5aZ4XX1WBKp6HZ/download\"\n", - " data_dir = tf.keras.utils.get_file(origin=dataset_url,fname='oa-sis' ,untar=True)\n", - " data_dir = pathlib.Path(data_dir)\n", - " \n", - " # unzip data to current directory \n", - " print (data_dir)\n", - " ! unzip /root/.keras/datasets/oa-sis.tar.gz" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "CsEL-j0w5Gd-" - }, - "outputs": [], - "source": [ - "def load_training (path):\n", - " # load training images (non segmented) in the path and store in numpy array\n", - " image_list = []\n", - " for filename in glob.glob(path+'/*.png'): \n", - " im=image.imread (filename)\n", - " image_list.append(im)\n", - "\n", - " print('train_X shape:',np.array(image_list).shape)\n", - " train_set = np.array(image_list, dtype=np.float32)\n", - " return train_set\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "hnIbIVop69Dn" - }, - "outputs": [], - "source": [ - "def process_training (data_set):\n", - " # the method normalizes training images and adds 4th dimention \n", - "\n", - " train_set = data_set\n", - " train_set = (train_set - np.mean(train_set))/ np.std(train_set)\n", - " train_set= (train_set- np.amin(train_set))/ np.amax(train_set- np.amin(train_set))\n", - " train_set = train_set [:,:,:,np.newaxis]\n", - " \n", - " return train_set" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "OQRjoY8HFUoi" - }, - "outputs": [], - "source": [ - "def load_labels (path):\n", - " # loads labels images and map pixel values to class indices and convert image data type to unit8 \n", - "\n", - " n_classes = 4\n", - " image_list =[]\n", - " for filename in glob.glob(path+'/*.png'): \n", - " im=image.imread (filename)\n", - " one_hot = np.zeros((im.shape[0], im.shape[1]))\n", - " for i, unique_value in enumerate(np.unique(im)):\n", - " one_hot[:, :][im == unique_value] = i\n", - " image_list.append(one_hot)\n", - "\n", - " print('train_y shape:',np.array(image_list).shape)\n", - " labels = np.array(image_list, dtype=np.uint8)\n", - " \n", - " pyplot.imshow(labels[2])\n", - " pyplot.show()\n", - "\n", - " return labels" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Xsmg4eHmLJY5" - }, - "outputs": [], - "source": [ - "def process_labels(seg_data):\n", - " # one hot encode label data and convert to numpy array\n", - " onehot_Y = []\n", - " for n in range(seg_data.shape[0]): \n", - " im = seg_data[n]\n", - " n_classes = 4\n", - " one_hot = np.zeros((im.shape[0], im.shape[1], n_classes),dtype=np.uint8)\n", - " for i, unique_value in enumerate(np.unique(im)):\n", - " one_hot[:, :, i][im == unique_value] = 1\n", - " onehot_Y.append(one_hot)\n", - " \n", - " onehot_Y =np.array(onehot_Y)\n", - " print (onehot_Y.dtype)\n", - " #print (np.unique(onehot_validate_Y))\n", - " print (onehot_Y.shape)\n", - "\n", - " return onehot_Y\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "jAFXCuq8hFaV" - }, - "outputs": [], - "source": [ - "# U-NET final model w on2Dtranspose and batch normalization after activation\n", - "# questions do I need to use shuffle =true in fit module ?\n", - "import tensorflow as tf\n", - "\n", - "def unet_model ():\n", - " filter_size=16 \n", - " input_layer = tf.keras.Input((256,256,1))\n", - " \n", - " pre_conv = tf.keras.layers.Conv2D(filter_size * 1, (3, 3), padding=\"same\")(input_layer)\n", - " pre_conv = tf.keras.layers.LeakyReLU(alpha=.01)(pre_conv)\n", - "\n", - "\n", - "# context module 1 pre-activation residual block\n", - " conv1 = tf.keras.layers.BatchNormalization()(pre_conv)\n", - " conv1 = tf.keras.layers.LeakyReLU(alpha=.01)(conv1)\n", - " conv1 = tf.keras.layers.Conv2D(filter_size * 1, (3, 3), padding=\"same\" )(conv1) \n", - " conv1 = tf.keras.layers.Dropout(.3) (conv1)\n", - " conv1 = tf.keras.layers.BatchNormalization()(conv1)\n", - " conv1 = tf.keras.layers.LeakyReLU(alpha=.01)(conv1)\n", - " conv1 = tf.keras.layers.Conv2D(filter_size * 1, (3, 3), padding=\"same\")(conv1) \n", - " conv1 = tf.keras.layers.Add()([pre_conv,conv1])\n", - " \n", - "# downsample and double number of feature maps \n", - " pool1 = tf.keras.layers.Conv2D(filter_size * 2, (3,3), (2,2) , padding='same')(conv1)\n", - " pool1 = tf.keras.layers.LeakyReLU(alpha=.01)(pool1)\n", - " \n", - "# context module 2\n", - " conv2 = tf.keras.layers.BatchNormalization()(pool1)\n", - " conv2 = tf.keras.layers.LeakyReLU(alpha=.01)(conv2)\n", - " conv2 = tf.keras.layers.Conv2D(filter_size * 2, (3, 3), padding=\"same\")(conv2)\n", - " conv2 = tf.keras.layers.Dropout(.3) (conv2) \n", - " conv2 = tf.keras.layers.BatchNormalization()(conv2)\n", - " conv2 = tf.keras.layers.LeakyReLU(alpha=.01)(conv2)\n", - " conv2 = tf.keras.layers.Conv2D(filter_size * 2, (3, 3), padding=\"same\")(conv2)\n", - " conv2 = tf.keras.layers.Add()([pool1,conv2])\n", - "\n", - "# downsample and double number of feature maps\n", - " pool2 = tf.keras.layers.Conv2D(filter_size*4, (3,3),(2,2), padding='same')(conv2)\n", - " pool2 = tf.keras.layers.LeakyReLU(alpha=.01)(pool2)\n", - "\n", - "# context module 3\n", - " conv3 = tf.keras.layers.BatchNormalization()(pool2)\n", - " conv3 = tf.keras.layers.LeakyReLU(alpha=.01)(conv3)\n", - " conv3 = tf.keras.layers.Conv2D(filter_size * 4, (3, 3), padding=\"same\")(conv3)\n", - " conv3 = tf.keras.layers.Dropout(.3) (conv3)\n", - " conv3 = tf.keras.layers.BatchNormalization()(conv3)\n", - " conv3 = tf.keras.layers.LeakyReLU(alpha=.01)(conv3)\n", - " conv3 = tf.keras.layers.Conv2D(filter_size * 4, (3, 3), padding=\"same\")(conv3)\n", - " conv3 = tf.keras.layers.Add()([pool2,conv3])\n", - "\n", - "# downsample and double number of feature maps\n", - " pool3 = tf.keras.layers.Conv2D(filter_size*8, (3,3),(2,2),padding='same')(conv3)\n", - " pool3 = tf.keras.layers.LeakyReLU(alpha=.01)(pool3)\n", - "\n", - "# context module 4\n", - " conv4 = tf.keras.layers.BatchNormalization()(pool3)\n", - " conv4 = tf.keras.layers.LeakyReLU(alpha=.01)(conv4)\n", - " conv4 = tf.keras.layers.Conv2D(filter_size * 8, (3, 3), padding=\"same\")(conv4)\n", - " conv4 = tf.keras.layers.Dropout(.3) (conv4)\n", - " conv4 = tf.keras.layers.BatchNormalization()(conv4)\n", - " conv4 = tf.keras.layers.LeakyReLU(alpha=.01)(conv4)\n", - " conv4 = tf.keras.layers.Conv2D(filter_size * 8, (3, 3), padding=\"same\")(conv4)\n", - " conv4 = tf.keras.layers.Add()([pool3,conv4])\n", - " print (\"conv4\",conv4.shape)\n", - "\n", - "# downsample and double number of feature maps\n", - " pool4 = tf.keras.layers.Conv2D(filter_size*16, (3,3),(2,2),padding='same')(conv4)\n", - " pool4 = tf.keras.layers.LeakyReLU(alpha=.01)(pool4) \n", - "\n", - "# context module 5\n", - " # Middle\n", - " convm = tf.keras.layers.BatchNormalization()(pool4)\n", - " convm = tf.keras.layers.LeakyReLU(alpha=.01)(convm)\n", - " convm = tf.keras.layers.Conv2D(filter_size * 16, (3, 3), padding=\"same\")(convm)\n", - " convm = tf.keras.layers.Dropout(.3) (convm)\n", - " convm = tf.keras.layers.BatchNormalization()(convm)\n", - " convm = tf.keras.layers.LeakyReLU(alpha=.01)(convm)\n", - " convm = tf.keras.layers.Conv2D(filter_size * 16, (3, 3), padding=\"same\")(convm)\n", - " convm = tf.keras.layers.Add()([pool4,convm])\n", - "\n", - "\n", - "#upsampling module 1\n", - " deconv4 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(convm)\n", - " deconv4 = tf.keras.layers.Conv2D (filter_size *8, (3, 3) , padding=\"same\")(deconv4)\n", - " deconv4 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv4) \n", - " print (\"upsample 1\",deconv4.shape)\n", - "\n", - "#concatatinate layers \n", - " uconv4 = tf.keras.layers.concatenate([deconv4, conv4], axis=3)\n", - "\n", - "\n", - "#localization module 1\n", - " uconv4 = tf.keras.layers.Conv2D(filter_size * 16, (3, 3) , padding=\"same\")(uconv4)\n", - " uconv4 = tf.keras.layers.BatchNormalization()(uconv4)\n", - " uconv4 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv4)\n", - " uconv4 = tf.keras.layers.Conv2D(filter_size * 8, (1, 1), padding=\"same\")(uconv4)\n", - " uconv4 = tf.keras.layers.BatchNormalization()(uconv4)\n", - " uconv4 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv4)\n", - "\n", - "#upsampling module 2\n", - " deconv3 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(uconv4)\n", - " deconv3 = tf.keras.layers.Conv2D (filter_size *4, (3, 3) , padding=\"same\")(deconv3)\n", - " deconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv3) \n", - "\n", - " \n", - "\n", - "# concatatinate layers \n", - " uconv3 = tf.keras.layers.concatenate([deconv3, conv3], axis=3)\n", - "\n", - "\n", - "# localization module 2\n", - " uconv3 = tf.keras.layers.Conv2D(filter_size * 8, (3, 3), padding=\"same\")(uconv3)\n", - " uconv3 = tf.keras.layers.BatchNormalization()(uconv3)\n", - " uconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv3)\n", - " uconv3 = tf.keras.layers.Conv2D(filter_size * 4, (1, 1), padding=\"same\")(uconv3)\n", - " uconv3 = tf.keras.layers.BatchNormalization()(uconv3)\n", - " uconv3 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv3)\n", - "\n", - "# segmentation layer 1\n", - " seg3 = tf.keras.layers.Conv2D(4, (3,3), activation=\"softmax\", padding='same' )(uconv3)\n", - "# upscale segmented layer 1\n", - " seg3 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(seg3)\n", - "\n", - "\n", - "# Upsample module 3\n", - " deconv2 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(uconv3)\n", - " deconv2 = tf.keras.layers.Conv2D (filter_size *2, (3, 3) , padding=\"same\")(deconv2)\n", - " deconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv2)\n", - "\n", - "\n", - "# concatination layer \n", - " uconv2 = tf.keras.layers.concatenate([deconv2, conv2], axis=3)\n", - "\n", - "\n", - "# localization module 3\n", - " uconv2 = tf.keras.layers.Conv2D(filter_size * 4, (3, 3), padding=\"same\")(uconv2)\n", - " uconv2 = tf.keras.layers.BatchNormalization()(uconv2)\n", - " uconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv2)\n", - " uconv2 = tf.keras.layers.Conv2D(filter_size * 2, (1, 1), padding=\"same\")(uconv2)\n", - " uconv2 = tf.keras.layers.BatchNormalization()(uconv2)\n", - " uconv2 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv2)\n", - "\n", - "# segmentation layer 2\n", - " seg2 = tf.keras.layers.Conv2D(4, (3,3), activation=\"softmax\", padding='same')(uconv2)\n", - "\n", - "# add segmentation layer 1 and 2\n", - " seg_32 = tf.keras.layers.Add()([seg3,seg2])\n", - "# upscale sum segmentation layer 1 and 2\n", - " seg_32 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(seg_32)\n", - "\n", - "\n", - "# Upsample module 4\n", - " deconv1 = tf.keras.layers.UpSampling2D(size=(2,2) , interpolation='bilinear')(uconv2)\n", - " deconv1 = tf.keras.layers.Conv2D (filter_size *1, (3, 3) , padding=\"same\")(deconv1)\n", - " deconv1 = tf.keras.layers.LeakyReLU(alpha=.01)(deconv1)\n", - "\n", - "\n", - "# concatination layer\n", - " uconv1 = tf.keras.layers.concatenate([deconv1, conv1], axis=3 )\n", - "\n", - "#final convolution layer\n", - " uconv1 = tf.keras.layers.Conv2D(filter_size * 2, (3, 3), padding=\"same\")(uconv1)\n", - " uconv1 = tf.keras.layers.BatchNormalization()(uconv1)\n", - " uconv1 = tf.keras.layers.LeakyReLU(alpha=.01)(uconv1)\n", - " \n", - "# final segmentation layer \n", - " seg1 = tf.keras.layers.Conv2D(4, (3,3), activation=\"softmax\", padding='same' )(uconv1)\n", - "\n", - "# sum all segmentation layers \n", - " seg_sum = tf.keras.layers.Add()([seg1,seg_32])\n", - "\n", - "\n", - " output_layer = tf.keras.layers.Conv2D(4, (3,3), padding='same' ,activation=\"softmax\")(seg_sum)\n", - " model = tf.keras.Model( input_layer , outputs=output_layer)\n", - "\n", - " \n", - " return model\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "aD1tLqv8Ax2o" - }, - "outputs": [], - "source": [ - " \n", - "def dice_coefficient (y_true, y_pred):\n", - " from keras import backend as k\n", - " y_true_f = k.flatten(y_true)\n", - " y_pred_f = k.flatten(y_pred) \n", - " \n", - " intersection1 = k.sum(y_true_f*y_pred_f)\n", - " coeff = (2.0*intersection1)/(k.sum(k.square(y_true_f)) + k.sum(k.square(y_pred_f)) )\n", - " return coeff\n", - " \n" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [], - "machine_shape": "hm", - "name": "Methods.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/recognition/MySolution/dataset.py b/recognition/MySolution/dataset.py new file mode 100644 index 0000000000..bfadd3a692 --- /dev/null +++ b/recognition/MySolution/dataset.py @@ -0,0 +1,106 @@ + +import numpy as np +import os +import cv2 +import random + + +class LoadData: + + def __init__(self): + self.load_training_data() + self.load_validation_data() + self.load_testing_data() + + def load_training_data(self): + DATADIR = "ADNI_AD_NC_2D/AD_NC/train" + CATEGORIES = ["AD", "NC"] + count = 0 + training_data = [] + for category in CATEGORIES: + path = os.path.join(DATADIR, category) + class_num = CATEGORIES.index(category) + for img in os.listdir(path): + img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE) + training_data.append([img_arr, class_num]) + count += 1 + if (count == 19368): + break + if (count == 19368): + break + print("Loaded " + str(count) + " train images", flush=True) + random.shuffle(training_data) + + X_train = [] + y_train = [] + + for features, label in training_data: + X_train.append(features) + y_train.append(label) + + X_train = np.array(X_train).reshape(-1, 240, 256, 1) + print(X_train.shape) + + np.save("X_train.npy", X_train) + np.save("y_train.npy", y_train) + + + def load_validation_data(self): + DATADIR = "ADNI_AD_NC_2D/AD_NC/train" + CATEGORIES = ["AD", "NC"] + count = 0 + training_data = [] + for category in CATEGORIES: + path = os.path.join(DATADIR, category) + class_num = CATEGORIES.index(category) + for img in os.listdir(path): + count += 1 + if (count <= 19368): + continue + img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE) + training_data.append([img_arr, class_num]) + + print("Loaded " + str(count - 19368) + " validation images", flush=True) + random.shuffle(training_data) + + X = [] + y = [] + + for features, label in training_data: + X.append(features) + y.append(label) + + X = np.array(X).reshape(-1, 240, 256, 1) + print(X.shape) + + np.save("X_validate.npy", X) + np.save("y_validate.npy", y) + + def load_testing_data(self): + DATADIR = "ADNI_AD_NC_2D/AD_NC/test" + CATEGORIES = ["AD", "NC"] + count = 0 + training_data = [] + for category in CATEGORIES: + path = os.path.join(DATADIR, category) + class_num = CATEGORIES.index(category) + for img in os.listdir(path): + img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE) + training_data.append([img_arr, class_num]) + count += 1 + + print("Loaded " + str(count) + " test images", flush=True) + random.shuffle(training_data) + + X_train = [] + y_train = [] + + for features, label in training_data: + X_train.append(features) + y_train.append(label) + + X_train = np.array(X_train).reshape(-1, 240, 256, 1) + print(X_train.shape) + + np.save("X_test.npy", X_train) + np.save("y_test.npy", y_train) \ No newline at end of file diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/recognition/MySolution/predict.py b/recognition/MySolution/predict.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/recognition/MySolution/unet_main.ipynb b/recognition/MySolution/unet_main.ipynb deleted file mode 100644 index 65eacd3904..0000000000 --- a/recognition/MySolution/unet_main.ipynb +++ /dev/null @@ -1,6323 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "zMdcaV1VIUXj" - }, - "outputs": [], - "source": [ - "%run '/content/drive/My Drive/Colab Notebooks/Methods.ipynb'" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "6mScdOyLBKiX", - "outputId": "1a4c974c-d0b7-4d5a-ada6-1c71b3b783a4" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_269_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_269_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_269_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_269_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_269_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_270_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_271_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_271_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_271_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_271_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_272_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_272_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_273_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_274_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_275_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_275_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_275_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_275_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_275_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_275_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_277_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_277_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_278_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_278_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_278_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_278_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_278_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_278_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_278_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_278_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_279_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_280_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_280_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_280_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_280_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_280_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_280_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_280_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_280_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_280_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_280_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_281_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_281_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_281_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_281_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_281_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_281_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_281_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_282_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_282_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_282_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_282_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_282_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_282_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_282_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_282_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_282_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_283_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_283_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_283_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_283_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_283_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_284_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_284_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_284_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_284_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_284_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_284_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_285_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_285_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_285_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_285_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_285_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_286_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_286_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_287_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_287_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_287_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_287_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_287_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_287_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_287_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_287_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_287_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_288_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_289_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_289_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_289_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_289_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_289_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_289_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_289_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_289_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_289_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_289_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_290_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_291_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_292_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_292_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_292_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_292_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_292_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_292_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_292_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_292_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_292_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_293_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_293_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_293_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_293_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_293_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_294_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_295_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_295_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_296_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_296_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_298_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_298_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_298_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_298_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_299_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_300_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_301_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_301_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_301_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_301_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_302_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_303_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_303_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_303_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_303_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_303_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_303_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_303_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_303_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_303_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_304_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_305_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_307_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_307_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_308_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_308_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_309_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_309_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_310_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_310_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_311_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_311_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_312_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_313_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_313_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_313_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_313_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_313_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_313_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_313_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_313_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_314_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_315_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_316_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_316_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_317_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_318_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_318_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_319_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_319_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_321_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_321_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_321_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_322_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_322_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_322_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_323_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_323_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_323_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_323_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_323_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_323_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_325_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_325_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_325_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_326_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_326_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_326_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_326_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_326_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_326_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_326_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_326_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_327_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_328_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_328_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_329_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_330_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_330_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_330_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_331_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_331_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_331_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_332_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_332_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_333_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_333_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_335_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_335_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_336_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_336_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_336_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_336_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_336_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_336_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_336_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_336_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_336_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_336_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_337_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_338_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_339_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_339_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_340_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_340_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_341_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_341_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_341_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_341_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_341_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_341_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_341_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_341_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_341_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_342_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_342_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_342_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_342_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_342_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_342_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_343_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_343_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_343_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_344_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_344_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_344_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_344_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_344_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_345_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_345_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_346_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_346_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_346_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_346_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_346_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_346_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_346_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_346_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_346_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_346_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_348_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_348_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_349_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_349_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_349_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_349_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_350_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_350_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_350_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_350_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_351_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_352_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_352_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_353_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_354_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_355_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_355_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_355_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_355_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_355_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_355_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_355_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_355_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_355_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_356_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_356_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_357_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_357_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_358_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_359_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_359_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_361_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_361_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_361_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_361_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_361_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_361_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_362_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_363_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_363_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_365_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_366_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_367_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_367_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_367_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_367_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_367_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_368_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_369_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_369_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_369_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_369_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_369_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_369_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_369_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_369_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_369_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_370_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_371_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_371_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_371_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_371_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_372_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_372_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_372_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_373_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_374_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_374_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_374_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_374_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_374_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_375_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_376_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_377_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_378_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_378_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_378_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_378_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_378_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_378_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_378_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_378_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_379_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_379_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_380_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_380_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_380_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_380_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_380_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_380_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_380_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_380_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_380_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_380_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_381_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_382_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_383_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_384_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_384_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_385_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_385_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_385_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_385_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_385_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_385_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_385_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_385_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_385_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_386_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_387_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_387_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_388_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_388_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_389_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_390_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_392_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_392_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_392_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_392_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_392_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_392_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_394_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_394_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_394_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_394_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_394_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_394_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_395_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_396_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_396_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_397_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_397_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_397_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_train/case_398_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_398_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_399_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_400_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_train/case_401_slice_9.nii.png \n", - " creating: keras_png_slices_data/keras_png_slices_validate/\n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_402_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_402_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_402_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_402_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_402_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_402_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_403_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_404_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_404_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_405_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_406_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_406_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_406_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_406_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_406_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_406_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_406_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_406_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_406_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_407_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_407_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_407_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_407_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_407_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_407_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_407_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_408_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_408_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_409_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_409_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_409_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_410_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_410_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_411_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_411_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_413_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_413_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_415_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_415_slice_9.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_416_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_416_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_416_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_416_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_417_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_12.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_417_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_417_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_417_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_418_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_419_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_419_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_419_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_419_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_419_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_420_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_420_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_420_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_420_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_420_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_420_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_420_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_420_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_420_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_421_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_21.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_24.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_29.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_4.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_422_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_422_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_423_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_423_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_424_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_425_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_17.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_31.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_6.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_426_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_426_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_428_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_428_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_428_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_429_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_15.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_429_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_429_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_429_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_429_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_429_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_429_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_429_slice_8.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_429_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_430_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_19.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_430_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_430_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_10.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_431_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_431_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_431_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_431_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_11.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_13.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_432_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_432_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_433_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_14.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_434_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_18.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_434_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_2.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_434_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_22.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_434_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_434_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_26.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_434_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_434_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_434_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_7.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_434_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_434_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_435_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_0.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_437_slice_1.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_437_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_437_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_437_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_3.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_437_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_5.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_437_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_437_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_438_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_16.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_439_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_20.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_439_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_23.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_439_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_25.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_439_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_27.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_439_slice_28.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_439_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_30.nii.png \n", - " extracting: keras_png_slices_data/keras_png_slices_validate/case_439_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_439_slice_9.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_0.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_1.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_10.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_11.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_12.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_13.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_14.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_15.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_16.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_17.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_18.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_19.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_2.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_20.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_21.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_22.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_23.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_24.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_25.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_26.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_27.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_28.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_29.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_3.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_30.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_31.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_4.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_5.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_6.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_7.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_8.nii.png \n", - " inflating: keras_png_slices_data/keras_png_slices_validate/case_440_slice_9.nii.png \n", - "train_X shape: (9664, 256, 256)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "%run '/content/drive/My Drive/Colab Notebooks/Methods.ipynb'\n", - "\n", - "# test script load and preprocess training images\n", - "\n", - "# download oasis data and unzip files \n", - "download_oasis()\n", - "\n", - "# load training data set \n", - "train_X = load_training ('/content/keras_png_slices_data/keras_png_slices_train')\n", - "\n", - "# check loaded image\n", - "pyplot.imshow(train_X[2])\n", - "pyplot.show()\n", - "\n", - "# pre- process training dataset \n", - "train_X = process_training(train_X)\n", - "\n", - "# load validaton data set and process it \n", - "validate_X = load_training ('/content/keras_png_slices_data/keras_png_slices_validate')\n", - "\n", - "# check loaded images\n", - "pyplot.imshow(validate_X[2])\n", - "pyplot.show()\n", - "\n", - "# pre process validation data set\n", - "validate_X = process_training(validate_X)\n", - "\n", - "\n", - "# load test data set and process it \n", - "test_X = load_training ('/content/keras_png_slices_data/keras_png_slices_test')\n", - "\n", - "# check loaded images\n", - "pyplot.imshow(test_X[2])\n", - "pyplot.show()\n", - "\n", - "# pre process test data set\n", - "test_X = process_training(test_X)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "NrqzzuUfDKVS", - "outputId": "c8137660-999b-4add-8821-c1df92cdf0df" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train_y shape: (9664, 256, 256)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# load and pre process segmented training set \n", - "train_Y = load_labels('/content/keras_png_slices_data/keras_png_slices_seg_train')\n", - "train_Y = process_labels(train_Y)\n", - "\n", - "# check loaded images\n", - "pyplot.imshow(train_Y[2,:,:,3])\n", - "pyplot.show()\n", - "\n", - "\n", - "# load and pre process segmented validation set \n", - "validate_Y = load_labels('/content/keras_png_slices_data/keras_png_slices_seg_validate')\n", - "validate_Y = process_labels(validate_Y)\n", - " \n", - "# check loaded images\n", - "pyplot.imshow(validate_Y[2,:,:,3])\n", - "pyplot.show()\n", - "\n", - "# load and pre process segmented test set \n", - "test_Y = load_labels('/content/keras_png_slices_data/keras_png_slices_seg_test')\n", - "test_Y = process_labels(test_Y)\n", - " \n", - "# check loaded images\n", - "pyplot.imshow(test_Y[2,:,:,3])\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "f7YdR2kaFaWt", - "outputId": "be180d59-ddcf-4596-c487-495e2f28d294" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "conv4 (None, 32, 32, 128)\n", - "upsample 1 (None, 32, 32, 128)\n", - "Epoch 1/200\n", - " 2/302 [..............................] - ETA: 27s - loss: 1.4847 - dice_coefficient: 0.1875WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0194s vs `on_train_batch_end` time: 0.1610s). Check your callbacks.\n", - "302/302 [==============================] - ETA: 0s - loss: 0.4356 - dice_coefficient: 0.4744WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0102s vs `on_test_batch_end` time: 0.0480s). Check your callbacks.\n", - "302/302 [==============================] - 57s 189ms/step - loss: 0.4356 - dice_coefficient: 0.4744 - val_loss: 0.3251 - val_dice_coefficient: 0.5224\n", - "Epoch 2/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2754 - dice_coefficient: 0.5611 - val_loss: 0.2666 - val_dice_coefficient: 0.5705\n", - "Epoch 3/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.2633 - dice_coefficient: 0.5741 - val_loss: 0.2589 - val_dice_coefficient: 0.5791\n", - "Epoch 4/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.2586 - dice_coefficient: 0.5795 - val_loss: 0.2559 - val_dice_coefficient: 0.5786\n", - "Epoch 5/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.2562 - dice_coefficient: 0.5825 - val_loss: 0.2534 - val_dice_coefficient: 0.5836\n", - "Epoch 6/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.2549 - dice_coefficient: 0.5841 - val_loss: 0.2528 - val_dice_coefficient: 0.5843\n", - "Epoch 7/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2539 - dice_coefficient: 0.5853 - val_loss: 0.2556 - val_dice_coefficient: 0.5840\n", - "Epoch 8/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2532 - dice_coefficient: 0.5863 - val_loss: 0.2517 - val_dice_coefficient: 0.5839\n", - "Epoch 9/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2530 - dice_coefficient: 0.5866 - val_loss: 0.2522 - val_dice_coefficient: 0.5898\n", - "Epoch 10/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2526 - dice_coefficient: 0.5871 - val_loss: 0.2503 - val_dice_coefficient: 0.5882\n", - "Epoch 11/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.2521 - dice_coefficient: 0.5877 - val_loss: 0.2522 - val_dice_coefficient: 0.5875\n", - "Epoch 12/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2518 - dice_coefficient: 0.5880 - val_loss: 0.2517 - val_dice_coefficient: 0.5844\n", - "Epoch 13/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2517 - dice_coefficient: 0.5882 - val_loss: 0.2511 - val_dice_coefficient: 0.5886\n", - "Epoch 14/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2512 - dice_coefficient: 0.5887 - val_loss: 0.2745 - val_dice_coefficient: 0.5823\n", - "Epoch 15/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2511 - dice_coefficient: 0.5890 - val_loss: 0.2511 - val_dice_coefficient: 0.5872\n", - "Epoch 16/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2507 - dice_coefficient: 0.5896 - val_loss: 0.2513 - val_dice_coefficient: 0.5896\n", - "Epoch 17/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.2504 - dice_coefficient: 0.5902 - val_loss: 0.2499 - val_dice_coefficient: 0.5880\n", - "Epoch 18/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2496 - dice_coefficient: 0.5911 - val_loss: 0.2516 - val_dice_coefficient: 0.5897\n", - "Epoch 19/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2488 - dice_coefficient: 0.5924 - val_loss: 0.3125 - val_dice_coefficient: 0.5689\n", - "Epoch 20/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2478 - dice_coefficient: 0.5939 - val_loss: 0.2527 - val_dice_coefficient: 0.5905\n", - "Epoch 21/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2464 - dice_coefficient: 0.5960 - val_loss: 1.0200 - val_dice_coefficient: 0.4830\n", - "Epoch 22/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.2449 - dice_coefficient: 0.5984 - val_loss: 0.2622 - val_dice_coefficient: 0.5885\n", - "Epoch 23/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2431 - dice_coefficient: 0.6011 - val_loss: 0.2537 - val_dice_coefficient: 0.5917\n", - "Epoch 24/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2410 - dice_coefficient: 0.6043 - val_loss: 0.2555 - val_dice_coefficient: 0.5896\n", - "Epoch 25/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2387 - dice_coefficient: 0.6077 - val_loss: 0.2561 - val_dice_coefficient: 0.5907\n", - "Epoch 26/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2361 - dice_coefficient: 0.6115 - val_loss: 0.2604 - val_dice_coefficient: 0.5890\n", - "Epoch 27/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2335 - dice_coefficient: 0.6157 - val_loss: 0.2645 - val_dice_coefficient: 0.5939\n", - "Epoch 28/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.2306 - dice_coefficient: 0.6201 - val_loss: 0.2633 - val_dice_coefficient: 0.5927\n", - "Epoch 29/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2276 - dice_coefficient: 0.6246 - val_loss: 0.2706 - val_dice_coefficient: 0.5950\n", - "Epoch 30/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2241 - dice_coefficient: 0.6299 - val_loss: 0.2708 - val_dice_coefficient: 0.5940\n", - "Epoch 31/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2213 - dice_coefficient: 0.6344 - val_loss: 0.2765 - val_dice_coefficient: 0.5961\n", - "Epoch 32/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2180 - dice_coefficient: 0.6395 - val_loss: 0.2799 - val_dice_coefficient: 0.5940\n", - "Epoch 33/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2154 - dice_coefficient: 0.6435 - val_loss: 0.2863 - val_dice_coefficient: 0.5967\n", - "Epoch 34/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2126 - dice_coefficient: 0.6480 - val_loss: 0.3128 - val_dice_coefficient: 0.5898\n", - "Epoch 35/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2103 - dice_coefficient: 0.6516 - val_loss: 0.2897 - val_dice_coefficient: 0.5963\n", - "Epoch 36/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2076 - dice_coefficient: 0.6558 - val_loss: 0.2906 - val_dice_coefficient: 0.5966\n", - "Epoch 37/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2055 - dice_coefficient: 0.6591 - val_loss: 0.2952 - val_dice_coefficient: 0.5977\n", - "Epoch 38/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2035 - dice_coefficient: 0.6624 - val_loss: 0.2950 - val_dice_coefficient: 0.5982\n", - "Epoch 39/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.2018 - dice_coefficient: 0.6651 - val_loss: 0.3019 - val_dice_coefficient: 0.5982\n", - "Epoch 40/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1999 - dice_coefficient: 0.6681 - val_loss: 0.3015 - val_dice_coefficient: 0.5999\n", - "Epoch 41/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1982 - dice_coefficient: 0.6708 - val_loss: 0.3041 - val_dice_coefficient: 0.5966\n", - "Epoch 42/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1969 - dice_coefficient: 0.6730 - val_loss: 0.3115 - val_dice_coefficient: 0.5995\n", - "Epoch 43/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1954 - dice_coefficient: 0.6753 - val_loss: 0.3092 - val_dice_coefficient: 0.5992\n", - "Epoch 44/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1944 - dice_coefficient: 0.6769 - val_loss: 0.3155 - val_dice_coefficient: 0.5994\n", - "Epoch 45/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1931 - dice_coefficient: 0.6791 - val_loss: 0.3091 - val_dice_coefficient: 0.6001\n", - "Epoch 46/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1922 - dice_coefficient: 0.6805 - val_loss: 0.3145 - val_dice_coefficient: 0.6004\n", - "Epoch 47/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1909 - dice_coefficient: 0.6825 - val_loss: 0.3165 - val_dice_coefficient: 0.5955\n", - "Epoch 48/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1898 - dice_coefficient: 0.6842 - val_loss: 0.3188 - val_dice_coefficient: 0.5996\n", - "Epoch 49/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1890 - dice_coefficient: 0.6856 - val_loss: 0.3187 - val_dice_coefficient: 0.5987\n", - "Epoch 50/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1883 - dice_coefficient: 0.6866 - val_loss: 0.3204 - val_dice_coefficient: 0.5993\n", - "Epoch 51/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1872 - dice_coefficient: 0.6885 - val_loss: 0.3194 - val_dice_coefficient: 0.5971\n", - "Epoch 52/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1862 - dice_coefficient: 0.6900 - val_loss: 0.3215 - val_dice_coefficient: 0.5989\n", - "Epoch 53/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1855 - dice_coefficient: 0.6912 - val_loss: 0.3248 - val_dice_coefficient: 0.6028\n", - "Epoch 54/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1848 - dice_coefficient: 0.6924 - val_loss: 0.3272 - val_dice_coefficient: 0.6008\n", - "Epoch 55/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1840 - dice_coefficient: 0.6934 - val_loss: 0.3275 - val_dice_coefficient: 0.5994\n", - "Epoch 56/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1833 - dice_coefficient: 0.6946 - val_loss: 0.3257 - val_dice_coefficient: 0.5983\n", - "Epoch 57/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1826 - dice_coefficient: 0.6957 - val_loss: 0.3320 - val_dice_coefficient: 0.6015\n", - "Epoch 58/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1820 - dice_coefficient: 0.6967 - val_loss: 0.3332 - val_dice_coefficient: 0.6016\n", - "Epoch 59/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1811 - dice_coefficient: 0.6981 - val_loss: 0.3337 - val_dice_coefficient: 0.6018\n", - "Epoch 60/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1804 - dice_coefficient: 0.6992 - val_loss: 0.3361 - val_dice_coefficient: 0.5992\n", - "Epoch 61/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1797 - dice_coefficient: 0.7002 - val_loss: 0.3363 - val_dice_coefficient: 0.5991\n", - "Epoch 62/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1792 - dice_coefficient: 0.7012 - val_loss: 0.3397 - val_dice_coefficient: 0.6010\n", - "Epoch 63/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1784 - dice_coefficient: 0.7022 - val_loss: 0.3409 - val_dice_coefficient: 0.5967\n", - "Epoch 64/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1778 - dice_coefficient: 0.7032 - val_loss: 0.3398 - val_dice_coefficient: 0.5992\n", - "Epoch 65/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1772 - dice_coefficient: 0.7041 - val_loss: 0.3413 - val_dice_coefficient: 0.5992\n", - "Epoch 66/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1764 - dice_coefficient: 0.7054 - val_loss: 0.3431 - val_dice_coefficient: 0.6001\n", - "Epoch 67/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1758 - dice_coefficient: 0.7064 - val_loss: 0.3434 - val_dice_coefficient: 0.5998\n", - "Epoch 68/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1755 - dice_coefficient: 0.7069 - val_loss: 0.3415 - val_dice_coefficient: 0.6008\n", - "Epoch 69/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1748 - dice_coefficient: 0.7080 - val_loss: 0.3451 - val_dice_coefficient: 0.6004\n", - "Epoch 70/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1742 - dice_coefficient: 0.7090 - val_loss: 0.3448 - val_dice_coefficient: 0.6002\n", - "Epoch 71/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1736 - dice_coefficient: 0.7099 - val_loss: 0.3461 - val_dice_coefficient: 0.6000\n", - "Epoch 72/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1732 - dice_coefficient: 0.7105 - val_loss: 0.3490 - val_dice_coefficient: 0.5994\n", - "Epoch 73/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1728 - dice_coefficient: 0.7112 - val_loss: 0.3507 - val_dice_coefficient: 0.5969\n", - "Epoch 74/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1718 - dice_coefficient: 0.7128 - val_loss: 0.3531 - val_dice_coefficient: 0.5997\n", - "Epoch 75/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1714 - dice_coefficient: 0.7133 - val_loss: 0.3505 - val_dice_coefficient: 0.6004\n", - "Epoch 76/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1711 - dice_coefficient: 0.7138 - val_loss: 0.3573 - val_dice_coefficient: 0.6029\n", - "Epoch 77/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1706 - dice_coefficient: 0.7147 - val_loss: 0.3542 - val_dice_coefficient: 0.6024\n", - "Epoch 78/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1699 - dice_coefficient: 0.7157 - val_loss: 0.3561 - val_dice_coefficient: 0.6027\n", - "Epoch 79/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1697 - dice_coefficient: 0.7162 - val_loss: 0.3550 - val_dice_coefficient: 0.6019\n", - "Epoch 80/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1690 - dice_coefficient: 0.7172 - val_loss: 0.3588 - val_dice_coefficient: 0.6038\n", - "Epoch 81/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1685 - dice_coefficient: 0.7179 - val_loss: 0.3601 - val_dice_coefficient: 0.6025\n", - "Epoch 82/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1681 - dice_coefficient: 0.7185 - val_loss: 0.3580 - val_dice_coefficient: 0.5991\n", - "Epoch 83/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1677 - dice_coefficient: 0.7193 - val_loss: 0.3613 - val_dice_coefficient: 0.6016\n", - "Epoch 84/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1671 - dice_coefficient: 0.7201 - val_loss: 0.3625 - val_dice_coefficient: 0.6015\n", - "Epoch 85/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1668 - dice_coefficient: 0.7206 - val_loss: 0.3594 - val_dice_coefficient: 0.6029\n", - "Epoch 86/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1664 - dice_coefficient: 0.7214 - val_loss: 0.3639 - val_dice_coefficient: 0.6024\n", - "Epoch 87/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1659 - dice_coefficient: 0.7220 - val_loss: 0.3643 - val_dice_coefficient: 0.6022\n", - "Epoch 88/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1655 - dice_coefficient: 0.7227 - val_loss: 0.3658 - val_dice_coefficient: 0.6006\n", - "Epoch 89/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1651 - dice_coefficient: 0.7234 - val_loss: 0.3630 - val_dice_coefficient: 0.5995\n", - "Epoch 90/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1646 - dice_coefficient: 0.7241 - val_loss: 0.3702 - val_dice_coefficient: 0.6023\n", - "Epoch 91/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1642 - dice_coefficient: 0.7248 - val_loss: 0.3688 - val_dice_coefficient: 0.6020\n", - "Epoch 92/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1639 - dice_coefficient: 0.7253 - val_loss: 0.3706 - val_dice_coefficient: 0.6012\n", - "Epoch 93/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1634 - dice_coefficient: 0.7260 - val_loss: 0.3677 - val_dice_coefficient: 0.6003\n", - "Epoch 94/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1630 - dice_coefficient: 0.7268 - val_loss: 0.3678 - val_dice_coefficient: 0.6029\n", - "Epoch 95/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1628 - dice_coefficient: 0.7271 - val_loss: 0.3706 - val_dice_coefficient: 0.6017\n", - "Epoch 96/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1624 - dice_coefficient: 0.7276 - val_loss: 0.3779 - val_dice_coefficient: 0.6031\n", - "Epoch 97/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1618 - dice_coefficient: 0.7285 - val_loss: 0.3755 - val_dice_coefficient: 0.6018\n", - "Epoch 98/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1614 - dice_coefficient: 0.7292 - val_loss: 0.3782 - val_dice_coefficient: 0.6044\n", - "Epoch 99/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1610 - dice_coefficient: 0.7298 - val_loss: 0.3729 - val_dice_coefficient: 0.6024\n", - "Epoch 100/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1609 - dice_coefficient: 0.7299 - val_loss: 0.3771 - val_dice_coefficient: 0.6036\n", - "Epoch 101/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1604 - dice_coefficient: 0.7308 - val_loss: 0.3779 - val_dice_coefficient: 0.6023\n", - "Epoch 102/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1599 - dice_coefficient: 0.7315 - val_loss: 0.3737 - val_dice_coefficient: 0.6008\n", - "Epoch 103/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1597 - dice_coefficient: 0.7319 - val_loss: 0.3779 - val_dice_coefficient: 0.6001\n", - "Epoch 104/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1595 - dice_coefficient: 0.7322 - val_loss: 0.3779 - val_dice_coefficient: 0.6018\n", - "Epoch 105/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1590 - dice_coefficient: 0.7330 - val_loss: 0.3786 - val_dice_coefficient: 0.6019\n", - "Epoch 106/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1587 - dice_coefficient: 0.7336 - val_loss: 0.3819 - val_dice_coefficient: 0.6024\n", - "Epoch 107/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1583 - dice_coefficient: 0.7341 - val_loss: 0.3790 - val_dice_coefficient: 0.6017\n", - "Epoch 108/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1582 - dice_coefficient: 0.7343 - val_loss: 0.3866 - val_dice_coefficient: 0.6029\n", - "Epoch 109/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1578 - dice_coefficient: 0.7350 - val_loss: 0.3808 - val_dice_coefficient: 0.6009\n", - "Epoch 110/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1575 - dice_coefficient: 0.7354 - val_loss: 0.3848 - val_dice_coefficient: 0.6029\n", - "Epoch 111/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1572 - dice_coefficient: 0.7359 - val_loss: 0.3824 - val_dice_coefficient: 0.6025\n", - "Epoch 112/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1567 - dice_coefficient: 0.7366 - val_loss: 0.3829 - val_dice_coefficient: 0.6019\n", - "Epoch 113/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1564 - dice_coefficient: 0.7371 - val_loss: 0.3864 - val_dice_coefficient: 0.6029\n", - "Epoch 114/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1565 - dice_coefficient: 0.7371 - val_loss: 0.3863 - val_dice_coefficient: 0.6011\n", - "Epoch 115/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1562 - dice_coefficient: 0.7376 - val_loss: 0.3848 - val_dice_coefficient: 0.6010\n", - "Epoch 116/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1558 - dice_coefficient: 0.7381 - val_loss: 0.3881 - val_dice_coefficient: 0.6026\n", - "Epoch 117/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1554 - dice_coefficient: 0.7388 - val_loss: 0.3883 - val_dice_coefficient: 0.6032\n", - "Epoch 118/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1552 - dice_coefficient: 0.7391 - val_loss: 0.3904 - val_dice_coefficient: 0.6022\n", - "Epoch 119/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1549 - dice_coefficient: 0.7396 - val_loss: 0.3895 - val_dice_coefficient: 0.6010\n", - "Epoch 120/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1546 - dice_coefficient: 0.7400 - val_loss: 0.3912 - val_dice_coefficient: 0.6038\n", - "Epoch 121/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1544 - dice_coefficient: 0.7405 - val_loss: 0.3922 - val_dice_coefficient: 0.6032\n", - "Epoch 122/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1542 - dice_coefficient: 0.7408 - val_loss: 0.3906 - val_dice_coefficient: 0.6027\n", - "Epoch 123/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1540 - dice_coefficient: 0.7411 - val_loss: 0.3913 - val_dice_coefficient: 0.6037\n", - "Epoch 124/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1537 - dice_coefficient: 0.7416 - val_loss: 0.3927 - val_dice_coefficient: 0.6001\n", - "Epoch 125/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1535 - dice_coefficient: 0.7419 - val_loss: 0.3954 - val_dice_coefficient: 0.6039\n", - "Epoch 126/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1531 - dice_coefficient: 0.7425 - val_loss: 0.3978 - val_dice_coefficient: 0.6031\n", - "Epoch 127/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1529 - dice_coefficient: 0.7427 - val_loss: 0.3929 - val_dice_coefficient: 0.6024\n", - "Epoch 128/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1525 - dice_coefficient: 0.7435 - val_loss: 0.4035 - val_dice_coefficient: 0.5998\n", - "Epoch 129/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1524 - dice_coefficient: 0.7437 - val_loss: 0.3977 - val_dice_coefficient: 0.6018\n", - "Epoch 130/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1522 - dice_coefficient: 0.7439 - val_loss: 0.3994 - val_dice_coefficient: 0.6034\n", - "Epoch 131/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1520 - dice_coefficient: 0.7443 - val_loss: 0.3970 - val_dice_coefficient: 0.6013\n", - "Epoch 132/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1518 - dice_coefficient: 0.7446 - val_loss: 0.3979 - val_dice_coefficient: 0.6024\n", - "Epoch 133/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1515 - dice_coefficient: 0.7450 - val_loss: 0.4007 - val_dice_coefficient: 0.6047\n", - "Epoch 134/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1513 - dice_coefficient: 0.7453 - val_loss: 0.4028 - val_dice_coefficient: 0.6021\n", - "Epoch 135/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1511 - dice_coefficient: 0.7457 - val_loss: 0.4010 - val_dice_coefficient: 0.6037\n", - "Epoch 136/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1507 - dice_coefficient: 0.7463 - val_loss: 0.4008 - val_dice_coefficient: 0.6026\n", - "Epoch 137/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1505 - dice_coefficient: 0.7466 - val_loss: 0.4030 - val_dice_coefficient: 0.6051\n", - "Epoch 138/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1504 - dice_coefficient: 0.7467 - val_loss: 0.4029 - val_dice_coefficient: 0.6016\n", - "Epoch 139/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1501 - dice_coefficient: 0.7472 - val_loss: 0.4025 - val_dice_coefficient: 0.6032\n", - "Epoch 140/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1499 - dice_coefficient: 0.7476 - val_loss: 0.4026 - val_dice_coefficient: 0.6017\n", - "Epoch 141/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1498 - dice_coefficient: 0.7477 - val_loss: 0.4050 - val_dice_coefficient: 0.6002\n", - "Epoch 142/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1495 - dice_coefficient: 0.7482 - val_loss: 0.4064 - val_dice_coefficient: 0.6025\n", - "Epoch 143/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1493 - dice_coefficient: 0.7485 - val_loss: 0.4074 - val_dice_coefficient: 0.6018\n", - "Epoch 144/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1492 - dice_coefficient: 0.7488 - val_loss: 0.4063 - val_dice_coefficient: 0.6000\n", - "Epoch 145/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1488 - dice_coefficient: 0.7494 - val_loss: 0.4092 - val_dice_coefficient: 0.6019\n", - "Epoch 146/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1487 - dice_coefficient: 0.7495 - val_loss: 0.4099 - val_dice_coefficient: 0.6026\n", - "Epoch 147/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1486 - dice_coefficient: 0.7497 - val_loss: 0.4096 - val_dice_coefficient: 0.6013\n", - "Epoch 148/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1483 - dice_coefficient: 0.7502 - val_loss: 0.4079 - val_dice_coefficient: 0.6036\n", - "Epoch 149/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1481 - dice_coefficient: 0.7504 - val_loss: 0.4092 - val_dice_coefficient: 0.6011\n", - "Epoch 150/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1480 - dice_coefficient: 0.7508 - val_loss: 0.4100 - val_dice_coefficient: 0.6027\n", - "Epoch 151/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1477 - dice_coefficient: 0.7512 - val_loss: 0.4056 - val_dice_coefficient: 0.6024\n", - "Epoch 152/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1476 - dice_coefficient: 0.7512 - val_loss: 0.4110 - val_dice_coefficient: 0.6039\n", - "Epoch 153/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1475 - dice_coefficient: 0.7515 - val_loss: 0.4125 - val_dice_coefficient: 0.6040\n", - "Epoch 154/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1472 - dice_coefficient: 0.7520 - val_loss: 0.4174 - val_dice_coefficient: 0.6022\n", - "Epoch 155/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1470 - dice_coefficient: 0.7523 - val_loss: 0.4126 - val_dice_coefficient: 0.6045\n", - "Epoch 156/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1468 - dice_coefficient: 0.7526 - val_loss: 0.4129 - val_dice_coefficient: 0.6018\n", - "Epoch 157/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1466 - dice_coefficient: 0.7529 - val_loss: 0.4132 - val_dice_coefficient: 0.6037\n", - "Epoch 158/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1464 - dice_coefficient: 0.7533 - val_loss: 0.4116 - val_dice_coefficient: 0.6020\n", - "Epoch 159/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1463 - dice_coefficient: 0.7535 - val_loss: 0.4131 - val_dice_coefficient: 0.6017\n", - "Epoch 160/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1462 - dice_coefficient: 0.7536 - val_loss: 0.4124 - val_dice_coefficient: 0.6015\n", - "Epoch 161/200\n", - "302/302 [==============================] - 57s 188ms/step - loss: 0.1460 - dice_coefficient: 0.7538 - val_loss: 0.4140 - val_dice_coefficient: 0.6016\n", - "Epoch 162/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1457 - dice_coefficient: 0.7543 - val_loss: 0.4178 - val_dice_coefficient: 0.6044\n", - "Epoch 163/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1457 - dice_coefficient: 0.7544 - val_loss: 0.4179 - val_dice_coefficient: 0.6030\n", - "Epoch 164/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1456 - dice_coefficient: 0.7546 - val_loss: 0.4143 - val_dice_coefficient: 0.6045\n", - "Epoch 165/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1453 - dice_coefficient: 0.7551 - val_loss: 0.4144 - val_dice_coefficient: 0.6043\n", - "Epoch 166/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1451 - dice_coefficient: 0.7553 - val_loss: 0.4182 - val_dice_coefficient: 0.6024\n", - "Epoch 167/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1450 - dice_coefficient: 0.7554 - val_loss: 0.4181 - val_dice_coefficient: 0.6020\n", - "Epoch 168/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1448 - dice_coefficient: 0.7558 - val_loss: 0.4164 - val_dice_coefficient: 0.6029\n", - "Epoch 169/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1446 - dice_coefficient: 0.7562 - val_loss: 0.4221 - val_dice_coefficient: 0.6032\n", - "Epoch 170/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1445 - dice_coefficient: 0.7563 - val_loss: 0.4232 - val_dice_coefficient: 0.6021\n", - "Epoch 171/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1444 - dice_coefficient: 0.7564 - val_loss: 0.4218 - val_dice_coefficient: 0.6043\n", - "Epoch 172/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1442 - dice_coefficient: 0.7568 - val_loss: 0.4215 - val_dice_coefficient: 0.6035\n", - "Epoch 173/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1439 - dice_coefficient: 0.7572 - val_loss: 0.4243 - val_dice_coefficient: 0.6037\n", - "Epoch 174/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1438 - dice_coefficient: 0.7575 - val_loss: 0.4209 - val_dice_coefficient: 0.6022\n", - "Epoch 175/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1437 - dice_coefficient: 0.7576 - val_loss: 0.4235 - val_dice_coefficient: 0.6032\n", - "Epoch 176/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1435 - dice_coefficient: 0.7579 - val_loss: 0.4219 - val_dice_coefficient: 0.6053\n", - "Epoch 177/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1434 - dice_coefficient: 0.7582 - val_loss: 0.4190 - val_dice_coefficient: 0.6028\n", - "Epoch 178/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1433 - dice_coefficient: 0.7583 - val_loss: 0.4248 - val_dice_coefficient: 0.6045\n", - "Epoch 179/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1432 - dice_coefficient: 0.7585 - val_loss: 0.4231 - val_dice_coefficient: 0.6032\n", - "Epoch 180/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1428 - dice_coefficient: 0.7590 - val_loss: 0.4228 - val_dice_coefficient: 0.6035\n", - "Epoch 181/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1428 - dice_coefficient: 0.7591 - val_loss: 0.4225 - val_dice_coefficient: 0.6038\n", - "Epoch 182/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1425 - dice_coefficient: 0.7595 - val_loss: 0.4249 - val_dice_coefficient: 0.6029\n", - "Epoch 183/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1424 - dice_coefficient: 0.7598 - val_loss: 0.4273 - val_dice_coefficient: 0.6029\n", - "Epoch 184/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1424 - dice_coefficient: 0.7597 - val_loss: 0.4267 - val_dice_coefficient: 0.6048\n", - "Epoch 185/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1421 - dice_coefficient: 0.7602 - val_loss: 0.4288 - val_dice_coefficient: 0.6029\n", - "Epoch 186/200\n", - "302/302 [==============================] - 56s 187ms/step - loss: 0.1419 - dice_coefficient: 0.7604 - val_loss: 0.4286 - val_dice_coefficient: 0.6036\n", - "Epoch 187/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1419 - dice_coefficient: 0.7605 - val_loss: 0.4306 - val_dice_coefficient: 0.6037\n", - "Epoch 188/200\n", - "302/302 [==============================] - 57s 187ms/step - loss: 0.1419 - dice_coefficient: 0.7606 - val_loss: 0.4256 - val_dice_coefficient: 0.6024\n", - "Epoch 189/200\n", - " 38/302 [==>...........................] - ETA: 47s - loss: 0.1409 - dice_coefficient: 0.7620Buffered data was truncated after reaching the output size limit." - ] - } - ], - "source": [ - "\n", - "# create model ,set training paramters , train the model\n", - "\n", - "# create a model instance and set training paramters \n", - "\n", - "model = unet_model()\n", - "opt= tf.keras.optimizers.Adam (learning_rate=.0005)\n", - "model.compile (optimizer=opt, loss= 'CategoricalCrossentropy' , metrics=[dice_coefficient])\n", - "\n", - "# set early stop criteria \n", - "#ES = tf.keras.callbacks.EarlyStopping( monitor='val_dice_coefficient',min_delta=.0001, patience=100, verbose=0, mode='max', restore_best_weights=True)\n", - "\n", - "# record history of training to display loss over ephocs \n", - "history = model.fit(train_X, train_Y, validation_data= (validate_X, validate_Y) ,batch_size=32,shuffle='True',epochs=200)\n", - "\n", - "# evaluate against testing data \n", - "model.evaluate(test_X,test_Y)\n", - "\n", - "# save trained model weights \n", - "model.save_weights('/content/drive/My Drive/modelweights/unet8')\n", - "\n", - "\n", - "# plot training and validation loss \n", - "pyplot.title('Dice Similarity Coefficient')\n", - "pyplot.plot(history.history['dice_coefficient'], color='blue', label='train')\n", - "pyplot.plot(history.history['val_dice_coefficient'], color='orange', label='test')\n", - "pyplot.legend(('training','validation'))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "Itu_OgukGCN3", - "outputId": "e9b2b708-d0a9-4586-8c3f-8c567a52d0d1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "prediction\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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hdvomcr5kSnOLtD8jmC7e2pmhxSWNRoLmbaJF4Bj2jy66vWJfVjrVdSYa2clbGebhR1ZA+X+BliUVGNxkddRqcNBmFfbT44Qt0b7Rqh06Z/nwVXDmDCcmMpTQdzcEMyzxRKnzMQAgJcKJKDUuqRNHOmRyaVgM216xf4WRGZyNPrC2Jfj+CxQZGIQQi4A7gPNSylbWslrAV0Aolj60gVLKZCGEAGYCfYE04FEpZeUf3F9CL19owdLDN2nKJ0avYdLtiZrylhsfRL++EcIE9WZW7svWkpImE7E7B7G13TK7z69M9aF2nIM0byXQff9deN6TjD6oBpdvzXS4XcLtHxJmHE7zKcYyuzUsT0W2MQghugApwGf5AsPbwGUp5ZtCiGeBmlLKKUKIvsB4LIGhEzBTStmpqEpUlTaG82NjyQjMe1xvUxaev2iHP2fc2ZHz7fJicmZYJsd65bVuZUsTkb+MxPeQpya3wb+BvmZNEiY0t/tcjWOSgM+d7yGQndsSOeMAs4NtL98eSuzK9l9aEL7ohOUqrWNrfvnu8yKP1+yfh8k641tkG0dFVJw2BqcaH4UQocCP+QLDIaCrlPKMEKI+sE5KGSWEmG/9fUnB7Qo7flUJDCWlr1uHA2+GkNDbtuk7LjOTh+c+QYO3/33BwZVE+1b0+mQDT9Y6xvoMM1OeHk31o9cxx1lGP+l8fGjxdwbT6zt3cXvVnE7bX8YTOazoOS8VSVkEhitSygDr7wJIllIGCCF+BN6UUv5jfW4tMEVKWegr+G8PDAD6AH/OfFqfnR2W2pSfMKYw4KWnqfXxv6sf3dUMIQ2R3p4IowljwnGb5+7Zf8FuKrzCXDWnc+OKSUSMrzyDn8q0V0JKKYUQxW6yFUKMAEYAeBdI8VXZ6AP8IbAWAMIsS9RVZ7pylZTUME15I4Mfxmquu6f+tyqsUfc238MUtg6oPf66aoiArFLWquIqaWA4J4Son+9WImfKYBIQkm+7htYyDSnlAmABWK4YSliPcmeoV5dD0xsQ380yqCnZlMaAYRPw+NU1l5mr0rzxPe/6gVGG4AZc65j3X6UzSrx/KH0SVqVqKGlgWAkMAd60/vw+X/k4IcRSLI2PV4tqX6jsrnQJI75b3tqOXsLAyR4ehP9qu92p52KRBV7t0NkHMCUno28aRuKg+vSO0A5nHvfbI0R+49rLVX1QEEfGN8ZYL+8bT5oF1drG2tZv3hFMFy649NwVUe/fJpLQ90NNebIpjV67HnXYQ1KVOdNduQToCgQKIU4BL2EJCMuEEMOA48BA6+arsfRIxGPprhzqhjpXaB0+mES4ndTrhgy4Hmrm2H15QeSmjgNJSQshLOgS+5tpB+G8ezmcsG/dMIzabCJwp6T6V3mBSHh4kjTRcvsZ0i+R1VGriY65l+Tr9TEn+hI+peq2cUSNiqPpG6OJfzBvDEPEF6PxPSUI/jaRZu89zMGbi+6xqErUkOhSMIQ15s5V23Ibrm54awz1P9iCNNofVaMP8Ce9UwRXx17XJGEpaG26nmn3D0Ju2+vyehdFH9WU9NAAnvvgU3r5ZHPCmMLMC11YN78TgQuqZoDQ+fqScXNeF6n3Pwcwp6YiOrTm0S9+5IHq2oS6TdYOpenD2lW5KiqX90q4W2UNDPoWkaxeY7nMTDNn0X/QSHR/F/1G0deogahe+GKv0mjEdM5xtqeyoK9bB2EwcKVzI357930um7O4aLKksv89tTm/ta+j2UdKiczMRBgMCEMhF6Q6Hc/t3UANYX9Q0YTDD+Db3/5dqDQaHQZfVzPedhNrv7A/g+qiKZXu7zxdaQajqbkS5SD2rUnU/du5N4jp2jW4VnS+gPKWE5j8lp3mlpoTefLJZfT2OUGg3pe2Xsd48ph24ZZvU2rw4X39ODjOj4R+2nwUWl52S/9qvQIcrAsT/ttjRL2brikXZy+VaTAN1PuSFizR+fi4PWNVWVOBQXFK4PyNfDY/hBc/uJdmLU7mLhhT0AC/awz4aYnd51zlWM9F0FNb3nz9w4Q/6enS+Sael9N5/WKUTTq7/I48NJeorNGEv7rTrXNIypoKDC4w+Uw7gnZrv8GqooixmzEJQfj7I0EH7W+IZ1n4WrefN2z144hMxxOrmjQ7zYHOnxPbeRTVl7ouMJjj9vP7hM4kv+tjk+siv0OPzSW8etFTwSsTFRhKSBgMZM+yBIPl224i8s+SJ0MRHVpz6aW8e+0so556dx8odR3dRsrcEX/XbmpJuw6jATB7iCKToxRHn4P9OL+sEQBRH+9AZjqe5HRufCzYn15Ravp1O4h74kbaNY9m0ZQZtPXS3v7sHjCDAZNiXLImRkWgAkNJCR0/N/ueUq3ZIwS37EqnqZdtq3eKOYMBonK8yeT2fQTl9HoKQa/tQ0A4Hql5YqLJpuvv9YtRrBsdY3dbj9PJBCVYekHK+5XQ/bmToD/h2W3DSG3ky9+z59s8X0144rWuLpm3ujYJb3lRgaGMCQ9PdH6+sLwa85oss5sYxOljWFWYtSmlRGwofIWtsO3e9K2WtwK1NBoR1+Psblucfod687bR94tu1Li+zakgIry80Ploh+LL9PRC2wrktr34bBfEeo5iw7t5Y1L0QsdjDf5hvu8NbknCU9ZUYChDuurViX+xFUceyhlIU/ygoPPx4dhzN3BoWN5gnJ6DhqL7s3L0p5szMsANjXQyOwtTsnNzF/Q1a3Lg7aZ2e02arB1Kk3kS/faDjgOElBjStWtR3O2bwoeramDuWvkDg1q7siw1Duau7iUf3iwMBk1QAJjxqevu66s6fY0aHHwl0mFX6tHuH/Pr15+Q8Fy7wsdhVHEqMJQh896D7JnQhjFJ0SXaX+fnqwkKAHX1Zk49H2tnD8WGTs/B2U059p/5RW56cPgcjrxjfyyQvmZNrgy9bve5qkIFhjIm1sdxdGwE0c+MYnum/UvfLq89Ybfh8drS2na3D9T7csd9lWP0XXk5PK8j+rV1OdZjkdP77Ln3fcTvwZp/fj/o2N3RvWM1ytu/91rJhVb3nsmQRydT8xMn5xFs2YP/Fjg7tQZgex/b/qXRBH2knf4sfg/mz6hvULG8ZG6/aTdzgos3zsBH58nPzVYVax+zrBq5M1RgKIV92Vm08fSmuacPWdVL94a4aErlthlPU//DjZqrBX3NmvSquw+9cBwUfHRZGIJDNeUyM/Nfk9m4IOHhib5OIEemB7K6wSe4O6i+fKEF5l4X3XqOsqICQwnJ7Cye7ftI7iSq4hIdWhOg2wHoSMhOoc/nTxM63c507fBQvD5JZVLNxEKP91LQftiqXcH1pQst+euZGHz2n60UqelLS3RoTWYtywCkC2092Tsxp2HWvUFh8pl27L3JDFSNrE4qMJSCuJrCXUf6sDLi52LvW+O907mrOb16pg+h/7W9DdHXrMnJYc2p0eMs65v+llt+ICuNuzeP4uHmW/hvYNGrTL0ctA8+3kfrd8fQ4J2qHRiye7XnrvfWFBlEXa3ngTvxuDcFqCDjSVxABYZSMCadRo5oSrfZ/Yu13+XHYphQL299y8FBGxn+yaM221SrnsG+GNtuyKvmdAa/8xShszewts8t+LyTxZO1HExBLKD1PQe4sjoS0/7DxaprZZIwQJRZUFh8vTavfXE/AGFfJGGsKIPMXEQFhlIyHYqn2rim+GSecnqk3sUYI/188hodu1cz2awr4cjtTz1BnaWW2w3Pn7fy87VbaPXZSXr5ZBe575dhf9At5HE8tXcb/xqZMpse48dxvp3OMvFp+UiCSzj/y/tyFiF/Wv4vyiYzRNlSiVpcyOvPetxfbyuft9Rme85x/vEOrHx+Gg2LMRS65ewxNF52BtPRRE3DpKFxCNLDEt8THqpP3PCZefURHjbbdhv6uN0FcKoKfWBtCKjh8PmcDN46Hx9Eg7rIcxcxX6/a4xHyU4layonpUS9i1x1n4HHHyUJ0bEEvijcUWgownzxtd2yD8fjJ3N8bvZzIXf+LxtAoGOlbjRW/fqEJDu5gqFcXWd0XU3xCuU78KrioryPmtDSITyiDGlVeKjC4gL55BNda1GLEa98S5uH4Q5+QncLq1OaMDTjpcBt79o+dQ+vsMfgfNeGRYsr91jff3Jb0utopwD3/729LLwXuCwpp/+lETpd94IREWtSIJ65HIKZLl912zsIYwhpztV293Mf6TInv5gSudQl3uI//jrOaxWdyZPXpQLav8z0ZQoLP8sqz+ExRVGAoJX3zCDJnZfJPi8KH2Sab0ujz2dME/5nFxekbrR9c5+2ZZGmI/CsDRn46BoCRA1eXeQs8wOlnYombODt3XMW9R3sQNzgK06UjZV6XHBlhgXR9cQOv1tkDWFbwunXtRBJ6O/5/abNlECHjG2q6ca88HMOs/71PRy/nA2umzKZD2ETq2+lyroxUG0MpGIIb4LUkm+X5uhMdOZCVxqRQy3yGwws7FJoPsfm8MXikwPUWWXbXOygpV7UxPHN0D92rmfjgSgjzP7yT4N+v5K4DWZ4Oz+9Awp3O5JnMk7PadU5bw+WhMbz4/Kfc5Vv8HI4njCn0+vgZGr9UMYODamMoI9Lfj+VNnRvgNGbURDzZytWHovmi+xzsDbgJXzGS0BVGGv+5DZmdRcOgIMI9HrPkOCyl8G9H0mzzQVyxSsWbjz7Ca146PK9kUn/bBrQTkMtH83eTmdS+PTPqOx/81rZYSV/vnpATGG6QJQoKYFlOcOWj07g7+2lCXq2YwcFZauB9GRn3vmUdiZrfxPHI+mF2t1l/53TMXjpktmX0nOnCBaLGHOKm7QMxycI/fiZpJtmUlvsvR7IpjSZrhxL19C5MV6665G/R/b0TjzXby2XNi8KYDsVzuEd1PrsWaPNaJJvSHL5+XcaMwHTRMoz5ysMxbB4w3eb5Kefa0q9db/q16829R3toXt+CIj18SW+UDTrH+SkrA3XFUEb2pjcELIlKdEnenDelUkdvu5BqfYMf6xYu5LZHH89d+9KcmkrgnYfps7Y/Pzf73u58iWxpotue+/DtkzfYKXRLNfRCcrRDBk3ZWWG+1d3NlJzM4mYNWUxDm/ImW73tTqLyvGLM7UkxeVpmquZ4+UIL4m4EOAfA9VvgAWIxhDTkvt9sJ7oNqp6U2wOUcMdCbhw1hjpzKu9Vg7piKAVxPY1JZ2xv2TJlNt3338Xz59rYlG+KrZn7e9j3aXyY3M7hcVcumq3Jm6jrfpKbd99nd/voHYNsggJAYsd0jnaoOunM3eHlCy3wuOz42z///1l+xpOnWNKsgc2/XVVjikQuFRhKQWZk8ltilE1Z8xXjMPQ4wa+zOzvc78hjng7XKSiM/92naLruUZuyJmuHEnSPc8OiFVsrPuyKeXfR803+jdStRGkE1WRfzGKbomYvH7PbwHf927oYzZY4/N+wHwo9bDXhSfxnbTXrIsrMTCInnOSm/4zOLYv6+iCmMlqurTLb88YNHH5vLZEeebcKPYduZN/vUcijx0le3pDYOnmNls3nj6FRunPjEo7M6kSUx0bcOW6krKnAUEKNNvvS2X+dw+frfHeIJt2GcvQ2y2Spf9osd/rYeqHjjU7L+Qjt0GrTxUs2C8u6YS3sKsln+WYuv+Nt89mdVm8nS5cncsnkx9iAvPaH5vPGEPr2Dszmol/d+Pei2XT3dPx1vkVuW5mowFAC/v/UZmFI3uybq+Z0ukybTPCn+zBdvYipWzs++vR9AnQGwLv8KupOQqCvZbkHPzY3mDXRebkoHzr4ED6D8tbmFP41mL/uixKf6qHRT+Cz6WipR1UmZgcS7W07C9KynoelLM2cRZulE2j6+hbMhV2FWf/2hLHN2HPfDHwKBIWwVcOJWrCl3NfCKA0VGIpJHxFOmxp5g3lyB7XM3IAJS4r4XxYvoiSp4XOYpJlPkjoDp0tdX7eIbkNKIx/Wz5iXrzDv713X6jvYU3Cnkr8e6xYuJNmUxqD/jERu1RwYsK7M7Vn4upWftIrgQtw5xte0HQZtkmbeutSchZu7EPnUpsI/0B1bkxriwz+z5gO/A57abYyizFbjdhcVGIrp8FR/VudLkPLk8buLHOnW+8AdXEi1/VaZ0eoruji4mIiJu5+a/cpveLEjxttu4nIzL754ZjotPauV6blr6hnIei8AABj1SURBVH14duliXpr0ON4/aHNinh7YFNHjMg0mNXY4/0FmZ/Fz71Z8PMc2S3dGlgch9+4lEsfLDJq6tuNSC28+fvo9u0vU5ViXrqPm7so9hgFUYCiVA1lpJHwRQSCF5/kTUwIIKjAYaNKo0aSEwr6HZ+Mh8t5ITX4fSuTo+Aoz7uDI7E5IveU79JHY9ZaMUOQFhTFJ0axZc6PTx+vcbS8fN/q7RHXpWs3M0GnfsbDaf/Bbph2TsLPDUmI7jaK6g8AAYDyVRNBdxTuvqVs7bnp3B2/VjQMcBwWAd072Jmiuk0mBKzAVGErhUHYdAufbvglSvwnUbBc8O5HTXb1tVjYKmreRIKDLrrGQb8hC1NpjmMo5R8DZSbGE3G2ZlnwkYq7NoKrIT0dTe0/exXaN+FTCtjj/QTjTvhUxkS2RAn5/6318dHYuxQvxaI3zeLzyNUvGdMQ80hfOnEe30pdBtYuer1ISurYtuO39v5lSu+JdwblTkYFBCLEIuAM4L6VsZS2bCgwHLlg3e15Kudr63HPAMCwN5hOklL+4od4V1vyoxYDtmogfNfqH2z1usbs0W40ltt985dbLIASifSseW7ySlp4baebhRbexo7gjfrDNZk0SdpdqbUa5bS81rL2C98Y9jLQO5Dr1io64jnkNlIVlxH6w+iUerP4Tv67yIE16cbdvCmCZaxK1ap9rrraE4J5954nw+oru1f59fT/OXDF8AswGPitQ/p6U8p38BUKIFsADQEugAbBGCBEppawyr6wpxUCaOcvhN93kDv1ZvUv77fXy7t95MayDu6tXbPratRA+Prz999dcMO1i5LaHCHvEMviqWuYWt97SmPblDfIKvs9AP71l1Wtdk8ZMXbXY0W65AvTZBJDGlkwYvHE4EeO3OdXFWBR9YG3u+usgowKSirVftjRx5np1AjlT6jqUtyIDg5TyLyFEqJPH6w8slVJmAglCiHigI1D5b7qsIkdu5amtXRwuXiIdLIRaXRSdl7EsGerVJfXGRpx80Ei/Znt5JakfyZ0vE8ruculmk0YjWFvyTfsPFzuINsH+itnFpY9qSt1PzxU7KACsSK1F4J1VI9luadoYxgkhHgG2AZOllMlAMJD/E3PKWqYhhBgBjADwRrsceWUQ7nGRqw8+hP/ivD9ZZmURHXcvm9p+49Qxzo+Npc4H7p9sk96/I5eb5f13Z9eQmMPSCf1Iz6G12UD5ZF6qSPQtozC9n1qixtFsaeLZ3x4ggqqRxamkgWEu8AogrT+nA48V5wBSygXAArAkailhPcpVG09vXnz5E6Z6DaXWIstFkczMxH9qNfjOdtsGBkH859rW+z9vfZtbo8cDELLUgNcqx11mpSH1ApmvF63mAYn/C675lq0qzkfXYlvzr0q0b6bMJmKcthu1sipRYJBSnsv5XQixEPjR+jAJCMm3aUNrWZXVzycD/fOLeCVlaG4Xmu5AIk0Xjyb+wbzRgP66ahzt/rGdI/jlln/SoQ5fXr4dsXGXy+vps3xzJb0uKxv6FpEMeXJ1iffv/twTBMgqc8dcstmVQoj6+R7eA+R00q8EHhBCeAkhwoAIoOqEUavjdwXYrFTdxyeTj6dN54VjcQw7nIDMyMS/BL1bj9Y4zztL5mNoHFL0xopLmfy8NCMinZUpswn844SLa1S+nOmuXAJ0BQKFEKeAl4CuQoi2WG4lEoGRAFLKfUKIZcB+LOtwjK1KPRI5jGfO8uO1ttyUL6FrpIcvkR4AV7kncSM6NlGSuNvG05sv/vmKB7s9aEnHrlRo+7LSmTBsHIZT28u7Ki5V5DtXSjlISllfSukhpWwopfxISvmwlLK1lLKNlPIuKeWZfNu/JqVsIqWMklL+5N7ql5+N7f0cpgvzEHq7/fAJ2SnMSA4t8tg19T48++sKTF0dJ3NRXEt/NZ23LkUUa5+f07wY9sITGNZWraAAKlFLmUk2pXH7J8/ww4TuhK0azsrUwu/4u3iD99TK3x9eWZgOxbN2VGdNRi57zhhTCFs1nP++8ZhNj1RVooZEl5A0ZtPyw3EcHD6n6I2BsyZyJ1tFroW3Bj5C6NszaONZcaZl6yObcOJNbX0yEqrTZHLV/ADkJ9bH8cPf0cwYWHiW6SNGPyKHu6f3qKJQgaGkpCT8q4uWgeFOGDtiAh7kveH8lm1i8pnR/Lh0gd1l5Nam6zFNqgllNIqu0WZf2vhts9sAd6p9CvO6W0YlbnyyI4bftZfOp1e0oH+YZUr04u2diBxWddfI/DdQC86Ugr5FJKvXaNeVMEkzyeZ0AHq+8TT1v43HdM7+epb6unVIvi2cH97OS1t+1SwZ3/4eTBcu2N3H1SK2ejE72LmBOSeMKaSa7ayJ4eGRG+DSzFkcz5eP4KmY/5DRPJjTo7MIfbRAfkqTyWZyWZnQ6dFVy7syGrdrO+EGywCvIL20yRRtz7cpNVgQ6Xjpu4pKLThTjkzSTP8j/cjuavmmr8OGQidGmc6dp8aS8zy4pGDy2LIJCgCNqxU+bTy/Rk6s0u2j86S5Z95cklXbf857skA37oTTHTjk1FtVS98yCumhL9YqWMLDk9MT2rN7csFbQOdGeWRLEwtbNgOqWFroAlTjYymI62mMS+pkU3bJnJ4bFCqLOVu6kSkr1lyOwoj2rbg8NIYJ33/HopULuDw0BuNtNzmxoyDpCXtBQSlIBYZSMJ48xc5pzicpqagiH9tGi6/Hl3c1CpXwegxHp0dzdHo0ge+fYutrc+njk0l9gx9bX5tLr5l/kXl74ROvhMGDuImzS1WPyJ9GIk1VbmiOhrqVUACIevkQDCz+fn0O9uPi4kYALH9pmlO3GjmOZqew87V2+BQy8ejki7HU6nyWHa3ew0/nuAdnSu0jLOrcg9BCRs6cWBKJXpR8IG6TZaNo9uI+l0ztruhUYFAAMF25QsfnRrPljblFb5zP9PBvWDc5EoD6+uLlgbxk9sJnheOgcPaJWL57fJp1LYjSd+suavcpULJ8jGErR9B86oFyz65VVtStRCn5fbOViM/zFoCpo/fltj2p6IOCcv9VlAVO9QH+TI7fx+cn1/P5yfUcntsxr35SUvOzTdzw1phiHbOlZzXGBpxkbMBJm9yVRTFJM1M79LH/pBBceSSG9ZPftVkgpijbH32PlPs6OXz+cFZdp48Fliua4Sc7c3vULUSNj3PZosCVgbpiKC2zCZ/Tgn1Z6bmZk6fUPsKUXXnN7x1eGI1/QmbuY6+j5wtNc+4uzddep5dPNmD5sCX0XwD94aapo6m1Px3dP3H4njWzPsNMZ2/3fmfMuhKO+ar9b199RDib35xLca8S/HTeSL1w+PyXrcN5MPF8oWnj/sqAP663AGBTTADmtJKnsavMVGBwgXozNzDQZzLLRtpPq771NdvL8xu3PoBcE+vUsQP3ZKBft8Ml9XRk+9S5JJvS6PP8ZAI+38jDXUZz7J75bj3nL/dHI7PtrBspBIn3F++b3VVWpXkz9fW83BrgeMHbqk4FBhdp+MYGBqdNZu1T04ocILOzw1JwMnPZ02dvJO6JG9H9udPhNkc+6IQ02E7oav7fxEIHSH1wJYRfLrRkZYRljEFNvQ9z/jeTx/0nEfKzkQ+6hTA24KRzlbTjgYTb2PlHFIsGf2Bz9dH3UF+Orm9Mk3PxdvcTBg92jZpFSdsCSmpThomXXx1KrU+qTk6F0lCBwYXqzdzA3aee5Gq4nj1PuKavfFq9nbw1O4WNl+2PtNMJM4eazNHc3z/UuisXbtaDnRb0dek6VozticeFNIZ+eEtuKrObvDwx9LmI9x2H+eGJNiUKDGvT9bz4wnD8D14jNG4jL2wcSVb1vLr5771C6N6NbsuGfduz69m5KQxjovP5EZJNaTw7fiI1V6mgkEMNiXYD4eWFrkljDkyuQXyfBUDh6dDdZcq5tsTlG2bRfLuBGfW3sfh6bT6LsiSDMTQMpv9vOxnhb1kO74wxhc2Z9Yj1PkedIq58cuRMP+/zwDA8zlwpVR4J4eHJD4kbi9WQWVDfnvfbZKC2HFgQHZdlXTDHVq8BQ9ySNauiKc6QaNUr4QYyMxPT/sNEDt9Jv9Bo+oXHsDLVh91ZZTsnoFcN23UeD3aCuMxMvEW2pTcCy8pMG682yf1w1zf4cbdvitNB4bwplfZvjKNfaDS6v3dWyOQy+tq1iNjiaTco7M7KwHC45LdMVZUKDO5kNiGzs5CZmXwQEckz9z7OmKRo1qa7/v75hDGFMUnRjEmK5nC2pSW9ti4NGXtD7jY5C60O8LuG+au8uQyno6+TZCpeQ9tVczpjkqK5deHT1Jm9AZldMecOGMIak7akht1JYh9drccz9wwr9SraVZFqYyhDcttejnaAKY+P4Eq3DH6/ZZZmpGDXvXdz6oJlefnIBudYHVV0gtLIP4dAog9hz1nukaeuv4Mvw/6grZcX9aYncC6m6Lp1+3O8g2S1Wq03DybljB+RY7bQCPenvi+uI0Nq4Zls6fXxirnEjlbazM+vX4xizVO34LFTTQ+3RwWGclD7w43U/hD2xAfSyGC5vRh64ha2fN+a0CWnaJJo6YFIHhwN7xR2JIsmw45gTsv7xj86vxkH/reK5p7aGYMPz3nCbsNoxOP7CZ82kmP3Ft1NGfJ0OqZ4xzMaZee2HBtjGU8Q9WIyxmOJRf8RLnTkIcejN3dnZTBo/pPU2ZmN569VO9lKaahbiXI0fcxDubMa/zrSlIZvbMhtTTeEhzL4hZKlzAz4bCMnjf4A/LfBT5yYmjdmouEsy5iI6eHfkDQlr1xmZtL0q0xKSx/ZhP4L1xLf7WPiu31MxxWH0fk6P3rRnTo9O5onho2l4Rsb8PxZBYXCqMBQjjzW7MTkoFfI5O9b4nTmANfNloFWkR6+ZIbafuBTzBm09KxGaqgRRL6RglI6Nf36hV+/tTvMWx/gz+u/fGnTzflS0H6G7tyL8HB+Ves06Zr2CpM0k2bOIuqj0fSLuZOAL7ZUycSt7qACQwVjCGuMrm0Lfvix4BrChYgM1RQtbNnM7ofcnJHBfXdYFg1L6L+AiyOic58TG3YR8+qEIk/X3ks7CsEQ1phpcT/T1stL89xAv6v0izuHoaHd1QptyOwsBvV8pMjtCvNrmgffpfrRZc+93BPSidAXN2I8ftLumA7FPtXGUEGEN7jI9QeiefD/Vlm/cZ3vuXjnu494MtS2hbGwnAG6a2m8dKElLwftQxacWuDEsBYdOtLubo/P8ryW/lbLj9sdDp5jfM3jbP26MfEzo3PPU33ZZih4xSQEZ7oHFV2JAtal65hxqicA2UO8MCaewI9jReylOKICQ3mSZlqtHcWxnov4rfkP8G7JDhOkN3NufCx1Z9nvIejTch/xndsi1lvWqjQeS+SXabfw8tv7MPW5guHHYIynnF9J0EPoefHtRUxf3hKAa4Oi6VL9kyL3+6zxX/DuX7mPI9uORhSIX1IPBx6ZTcGL2TRzFi1XjXV47JpxBoLmqpGLrqICQ3mSkuZTL0HPwjdrsnYo1bdUY80z9udh1NH70vuxDcTNsr//nOBNtI5pR4P1eWW1Np8nOu5ednVcQq/GjyKKERgAWnsmc+zNGILiJOOmfk0/n+IP3jo8xFHvgW1QaPrlKKof0xE5p+J1jVZVKjCUM9OpM/QY/BhHBxpIuHuBzXNjkqI5/HQLmh1Mwnj2HIN2jiW5WTW2/a94yVTsnvfIMS7tjoG28J+Fv/F9p3DMxUhCUt/gx9rB0zh4b03rVG7Xa7NlEHVmVCNi+75i1U0pPRUYypnMzkK/bgfNNnnT96UClw6Zmeiv7SAnEbvunzhqb9DT9/ueHHwpnE39Lfcee7JqsKebP2CbSOScKZNGBktK97UTp3FlPEzq8TDyrGXWpdnTcn8/KiCJHwIskypMXtp8Bs+fa0Ncr7ocnhlMfNdPcssbGfxoZHBtULhoSuWXtEYsvrkdwWknMKemYn8hQMWd1CSqKswQ0pCRv//OXb4lzyuQKbO5eeeD1LrjMMfejuGvB6ZR38m8jr+meXAkq16xzre6V5titXcozivOJCoVGKo4GXMDXedv4vnAQ0VvbEfrzYNpcE/eKMf4GdFsHjDdYc6JE8YUBu+3dDcaZtbG6yc1kKiiUIFBsZHdqz0nehuY3f9j+vg4P7ox7OfHiRoRlzv5Ksfxl2PJrJ9Nwh0LmXSmPT+uy3uveSbrCHlVNRJWRCowKHal9+9Iei3L+IhL7cwcG2A7L2Jlqg+vvD4k93HQiv0OE6DqfH25OLANNQ+n53aDKhWbCgxKkfQB/pibhtiU6dKztQlOlCpDrV2pFMl05Spss70aUAOGlRxFzpUQQoQIIf4QQuwXQuwTQky0ltcSQvwmhDhi/VnTWi6EEO8LIeKFELuFEO3c/UcoiuJazkyiMgKTpZQtgGhgrBCiBfAssFZKGQGstT4GuB2IsP4bAZR+NI6iKGWqyMAgpTwjpdxh/f06cAAIBvoDn1o3+xS42/p7f+AzabEJCBBC1Hd5zRVFcZtiTbsWQoQCNwKbgbpSypz13s8COauEBAP5s2uespYpilJJOB0YhBB+wLfAJCnltfzPSUvXRrG6N4QQI4QQ24QQ27IpfeYgRVFcx6nAIITwwBIUFkspl1uLz+XcIlh/nreWJwH5+8EaWstsSCkXSCnbSynbe6BN7qEoSvlxpldCAB8BB6SU+TMGrARyRsMMAb7PV/6ItXciGria75ZDUZRKwJlxDJ2Bh4E9QoicIW7PA28Cy4QQw4DjwEDrc6uBvkA8llVBh7q0xoqiuF2RgUFK+Q/gaG1xzXBFa3uD41Q7iqJUeCoZrKIoGiowKIqioQKDoigaKjAoiqKhAoOiKBoqMCiKoqECg6IoGiowKIqioQKDoigaKjAoiqKhAoOiKBoqMCiKoqECg6IoGiowKIqioQKDoigaKjAoiqKhAoOiKBoqMCiKoqECg6IoGiowKIqioQKDoigaKjAoiqKhAoOiKBoqMCiKoqECg6IoGiowKIqioQKDoigaKjAoiqKhAoOiKBoqMCiKoqECg6IoGiowKIqioQKDoigaKjAoiqJRZGAQQoQIIf4QQuwXQuwTQky0lk8VQiQJIeKs//rm2+c5IUS8EOKQEKK3O/8ARVFcz+DENkZgspRyhxCiOrBdCPGb9bn3pJTv5N9YCNECeABoCTQA1gghIqWUJldWXFEU9ynyikFKeUZKucP6+3XgABBcyC79gaVSykwpZQIQD3R0RWUVRSkbxWpjEEKEAjcCm61F44QQu4UQi4QQNa1lwcDJfLudwk4gEUKMEEJsE0Jsyyaz2BVXFMV9nA4MQgg/4FtgkpTyGjAXaAK0Bc4A04tzYinlAilleyllew+8irOroihu5lRgEEJ4YAkKi6WUywGklOeklCYppRlYSN7tQhIQkm/3htYyRVEqCWd6JQTwEXBASvluvvL6+Ta7B9hr/X0l8IAQwksIEQZEAFtcV2VFUdzNmV6JzsDDwB4hRJy17HlgkBCiLSCBRGAkgJRynxBiGbAfS4/GWNUjoSiVi5BSlncdEEJcAFKBi+VdFycEUjnqCZWnrqqermevro2llEHO7FwhAgOAEGKblLJ9edejKJWlnlB56qrq6XqlrasaEq0oioYKDIqiaFSkwLCgvCvgpMpST6g8dVX1dL1S1bXCtDEoilJxVKQrBkVRKohyDwxCiD7W6dnxQohny7s+BQkhEoUQe6xTy7dZy2oJIX4TQhyx/qxZ1HHcUK9FQojzQoi9+crs1ktYvG99jXcLIdpVgLpWuGn7haQYqFCva5mkQpBSlts/QA8cBcIBT2AX0KI862SnjolAYIGyt4Fnrb8/C7xVDvXqArQD9hZVL6Av8BMggGhgcwWo61TgKTvbtrC+D7yAMOv7Q19G9awPtLP+Xh04bK1PhXpdC6mny17T8r5i6AjESymPSSmzgKVYpm1XdP2BT62/fwrcXdYVkFL+BVwuUOyoXv2Bz6TFJiCgwJB2t3JQV0fKbdq+dJxioEK9roXU05Fiv6blHRicmqJdziTwqxBiuxBihLWsrpTyjPX3s0Dd8qmahqN6VdTXucTT9t2tQIqBCvu6ujIVQn7lHRgqg5ullO2A24GxQogu+Z+Ulmu1Cte1U1HrlU+ppu27k50UA7kq0uvq6lQI+ZV3YKjwU7SllEnWn+eBFVguwc7lXDJaf54vvxracFSvCvc6ywo6bd9eigEq4Ovq7lQI5R0YtgIRQogwIYQnllyRK8u5TrmEEL7WPJcIIXyBXliml68Ehlg3GwJ8Xz411HBUr5XAI9ZW9Gjgar5L43JREaftO0oxQAV7XR3V06WvaVm0ohbRwtoXS6vqUeCF8q5PgbqFY2nN3QXsy6kfUBtYCxwB1gC1yqFuS7BcLmZjuWcc5qheWFrNP7C+xnuA9hWgrp9b67Lb+satn2/7F6x1PQTcXob1vBnLbcJuIM76r29Fe10LqafLXlM18lFRFI3yvpVQFKUCUoFBURQNFRgURdFQgUFRFA0VGBRF0VCBQVEUDRUYFEXRUIFBURSN/wc5RTo3n47HvwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tf.Tensor(0.62088764, shape=(), dtype=float32)\n" - ] - } - ], - "source": [ - "# validate output \n", - "out = model.predict(test_X)\n", - "out_r = np.round(out)\n", - "out_argmax = np.argmax (out,-1)\n", - "gt_test_Y = np.argmax(test_Y,-1)\n", - "\n", - "im = 5\n", - "\n", - "for i in range (4):\n", - " print(\"prediction\")\n", - " pyplot.imshow(out_r[im,:,:,i])\n", - " pyplot.show()\n", - " print(\"ground truth\")\n", - " pyplot.imshow(test_Y[im,:,:,i])\n", - " pyplot.show()\n", - "\n", - "print (\"prediction\")\n", - "pyplot.imshow(out_argmax[im,:,:])\n", - "pyplot.show()\n", - "\n", - "print (\"ground truth\")\n", - "pyplot.imshow(gt_test_Y [im,:,:])\n", - "pyplot.show()\n", - "\n", - "# calculate Dice Similarity coefficient on test data set\n", - "gt = tf.convert_to_tensor(test_Y,dtype=tf.float32)\n", - "print (dice_coefficient(gt,out_r))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "Mz_oMBjTv0Lg" - }, - "outputs": [], - "source": [ - "from keras import backend as k\n", - "\n", - "def dice_coef(y_true, y_pred, smooth=1):\n", - " \n", - " \n", - " y_true_f = k.flatten(y_true)\n", - " y_pred_f = k.flatten(y_pred) \n", - " \n", - " intersection1 = k.sum(y_true_f*y_pred_f)\n", - " coeff = (2.0*intersection1)/(k.sum(k.square(y_true_f)) + k.sum(k.square(y_pred_f)) )\n", - "\n", - " \n", - " return coeff" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "84BDzPgcv0lZ", - "outputId": "e4182a16-2e09-43ef-9562-9b4598149098" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tf.Tensor(0.89720505, shape=(), dtype=float32)\n" - ] - } - ], - "source": [ - "gt = tf.convert_to_tensor(test_Y,dtype=tf.float32)\n", - "print (dice_coef(gt,out))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "9EgJEYGvGCeo" - }, - "outputs": [], - "source": [ - "'''\n", - "# load saved model and evaluate results\n", - "loaded_model = unet_model()\n", - "opt= tf.keras.optimizers.Adam (learning_rate=.0005)\n", - "loaded_model.compile (optimizer=opt, loss= 'CategoricalCrossentropy' , metrics=['accuracy'])\n", - "loaded_model.load_weights('/content/drive/My Drive/modelweights/unet8')\n", - "loaded_model.evaluate (test_X,test_Y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "rnErLdOardB7", - "outputId": "b7492520-113e-4402-fbdb-0b952cbd2a0a" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"functional_3\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "input_2 (InputLayer) [(None, 256, 256, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "conv2d_30 (Conv2D) (None, 256, 256, 16) 160 input_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_26 (LeakyReLU) (None, 256, 256, 16) 0 conv2d_30[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_16 (BatchNo (None, 256, 256, 16) 64 leaky_re_lu_26[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_27 (LeakyReLU) (None, 256, 256, 16) 0 batch_normalization_16[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_31 (Conv2D) (None, 256, 256, 16) 2320 leaky_re_lu_27[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_5 (Dropout) (None, 256, 256, 16) 0 conv2d_31[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_17 (BatchNo (None, 256, 256, 16) 64 dropout_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_28 (LeakyReLU) (None, 256, 256, 16) 0 batch_normalization_17[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_32 (Conv2D) (None, 256, 256, 16) 2320 leaky_re_lu_28[0][0] \n", - "__________________________________________________________________________________________________\n", - "add_7 (Add) (None, 256, 256, 16) 0 leaky_re_lu_26[0][0] \n", - " conv2d_32[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_33 (Conv2D) (None, 128, 128, 32) 4640 add_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_29 (LeakyReLU) (None, 128, 128, 32) 0 conv2d_33[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_18 (BatchNo (None, 128, 128, 32) 128 leaky_re_lu_29[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_30 (LeakyReLU) (None, 128, 128, 32) 0 batch_normalization_18[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_34 (Conv2D) (None, 128, 128, 32) 9248 leaky_re_lu_30[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_6 (Dropout) (None, 128, 128, 32) 0 conv2d_34[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_19 (BatchNo (None, 128, 128, 32) 128 dropout_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_31 (LeakyReLU) (None, 128, 128, 32) 0 batch_normalization_19[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_35 (Conv2D) (None, 128, 128, 32) 9248 leaky_re_lu_31[0][0] \n", - "__________________________________________________________________________________________________\n", - "add_8 (Add) (None, 128, 128, 32) 0 leaky_re_lu_29[0][0] \n", - " conv2d_35[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_36 (Conv2D) (None, 64, 64, 64) 18496 add_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_32 (LeakyReLU) (None, 64, 64, 64) 0 conv2d_36[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_20 (BatchNo (None, 64, 64, 64) 256 leaky_re_lu_32[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_33 (LeakyReLU) (None, 64, 64, 64) 0 batch_normalization_20[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_37 (Conv2D) (None, 64, 64, 64) 36928 leaky_re_lu_33[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_7 (Dropout) (None, 64, 64, 64) 0 conv2d_37[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_21 (BatchNo (None, 64, 64, 64) 256 dropout_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_34 (LeakyReLU) (None, 64, 64, 64) 0 batch_normalization_21[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_38 (Conv2D) (None, 64, 64, 64) 36928 leaky_re_lu_34[0][0] \n", - "__________________________________________________________________________________________________\n", - "add_9 (Add) (None, 64, 64, 64) 0 leaky_re_lu_32[0][0] \n", - " conv2d_38[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_39 (Conv2D) (None, 32, 32, 128) 73856 add_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_35 (LeakyReLU) (None, 32, 32, 128) 0 conv2d_39[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_22 (BatchNo (None, 32, 32, 128) 512 leaky_re_lu_35[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_36 (LeakyReLU) (None, 32, 32, 128) 0 batch_normalization_22[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_40 (Conv2D) (None, 32, 32, 128) 147584 leaky_re_lu_36[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_8 (Dropout) (None, 32, 32, 128) 0 conv2d_40[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_23 (BatchNo (None, 32, 32, 128) 512 dropout_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_37 (LeakyReLU) (None, 32, 32, 128) 0 batch_normalization_23[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_41 (Conv2D) (None, 32, 32, 128) 147584 leaky_re_lu_37[0][0] \n", - "__________________________________________________________________________________________________\n", - "add_10 (Add) (None, 32, 32, 128) 0 leaky_re_lu_35[0][0] \n", - " conv2d_41[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_42 (Conv2D) (None, 16, 16, 256) 295168 add_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_38 (LeakyReLU) (None, 16, 16, 256) 0 conv2d_42[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_24 (BatchNo (None, 16, 16, 256) 1024 leaky_re_lu_38[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_39 (LeakyReLU) (None, 16, 16, 256) 0 batch_normalization_24[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_43 (Conv2D) (None, 16, 16, 256) 590080 leaky_re_lu_39[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_9 (Dropout) (None, 16, 16, 256) 0 conv2d_43[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_25 (BatchNo (None, 16, 16, 256) 1024 dropout_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_40 (LeakyReLU) (None, 16, 16, 256) 0 batch_normalization_25[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_44 (Conv2D) (None, 16, 16, 256) 590080 leaky_re_lu_40[0][0] \n", - "__________________________________________________________________________________________________\n", - "add_11 (Add) (None, 16, 16, 256) 0 leaky_re_lu_38[0][0] \n", - " conv2d_44[0][0] \n", - "__________________________________________________________________________________________________\n", - "up_sampling2d_6 (UpSampling2D) (None, 32, 32, 256) 0 add_11[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_45 (Conv2D) (None, 32, 32, 128) 295040 up_sampling2d_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_41 (LeakyReLU) (None, 32, 32, 128) 0 conv2d_45[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_4 (Concatenate) (None, 32, 32, 256) 0 leaky_re_lu_41[0][0] \n", - " add_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_46 (Conv2D) (None, 32, 32, 256) 590080 concatenate_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_26 (BatchNo (None, 32, 32, 256) 1024 conv2d_46[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_42 (LeakyReLU) (None, 32, 32, 256) 0 batch_normalization_26[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_47 (Conv2D) (None, 32, 32, 128) 32896 leaky_re_lu_42[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_27 (BatchNo (None, 32, 32, 128) 512 conv2d_47[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_43 (LeakyReLU) (None, 32, 32, 128) 0 batch_normalization_27[0][0] \n", - "__________________________________________________________________________________________________\n", - "up_sampling2d_7 (UpSampling2D) (None, 64, 64, 128) 0 leaky_re_lu_43[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_48 (Conv2D) (None, 64, 64, 64) 73792 up_sampling2d_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_44 (LeakyReLU) (None, 64, 64, 64) 0 conv2d_48[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_5 (Concatenate) (None, 64, 64, 128) 0 leaky_re_lu_44[0][0] \n", - " add_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_49 (Conv2D) (None, 64, 64, 128) 147584 concatenate_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_28 (BatchNo (None, 64, 64, 128) 512 conv2d_49[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_45 (LeakyReLU) (None, 64, 64, 128) 0 batch_normalization_28[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_50 (Conv2D) (None, 64, 64, 64) 8256 leaky_re_lu_45[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_29 (BatchNo (None, 64, 64, 64) 256 conv2d_50[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_46 (LeakyReLU) (None, 64, 64, 64) 0 batch_normalization_29[0][0] \n", - "__________________________________________________________________________________________________\n", - "up_sampling2d_9 (UpSampling2D) (None, 128, 128, 64) 0 leaky_re_lu_46[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_52 (Conv2D) (None, 128, 128, 32) 18464 up_sampling2d_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_47 (LeakyReLU) (None, 128, 128, 32) 0 conv2d_52[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_6 (Concatenate) (None, 128, 128, 64) 0 leaky_re_lu_47[0][0] \n", - " add_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_53 (Conv2D) (None, 128, 128, 64) 36928 concatenate_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_30 (BatchNo (None, 128, 128, 64) 256 conv2d_53[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_48 (LeakyReLU) (None, 128, 128, 64) 0 batch_normalization_30[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_54 (Conv2D) (None, 128, 128, 32) 2080 leaky_re_lu_48[0][0] \n", - "__________________________________________________________________________________________________\n", - "batch_normalization_31 (BatchNo (None, 128, 128, 32) 128 conv2d_54[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_49 (LeakyReLU) (None, 128, 128, 32) 0 batch_normalization_31[0][0] \n", - "__________________________________________________________________________________________________\n", - "up_sampling2d_11 (UpSampling2D) (None, 256, 256, 32) 0 leaky_re_lu_49[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_56 (Conv2D) (None, 256, 256, 16) 4624 up_sampling2d_11[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_50 (LeakyReLU) (None, 256, 256, 16) 0 conv2d_56[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_7 (Concatenate) (None, 256, 256, 32) 0 leaky_re_lu_50[0][0] \n", - " add_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_51 (Conv2D) (None, 64, 64, 4) 2308 leaky_re_lu_46[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_57 (Conv2D) (None, 256, 256, 32) 9248 concatenate_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "up_sampling2d_8 (UpSampling2D) (None, 128, 128, 4) 0 conv2d_51[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_55 (Conv2D) (None, 128, 128, 4) 1156 leaky_re_lu_49[0][0] \n", - "__________________________________________________________________________________________________\n", - "leaky_re_lu_51 (LeakyReLU) (None, 256, 256, 32) 0 conv2d_57[0][0] \n", - "__________________________________________________________________________________________________\n", - "add_12 (Add) (None, 128, 128, 4) 0 up_sampling2d_8[0][0] \n", - " conv2d_55[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_58 (Conv2D) (None, 256, 256, 4) 1156 leaky_re_lu_51[0][0] \n", - "__________________________________________________________________________________________________\n", - "up_sampling2d_10 (UpSampling2D) (None, 256, 256, 4) 0 add_12[0][0] \n", - "__________________________________________________________________________________________________\n", - "add_13 (Add) (None, 256, 256, 4) 0 conv2d_58[0][0] \n", - " up_sampling2d_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_59 (Conv2D) (None, 256, 256, 4) 148 add_13[0][0] \n", - "==================================================================================================\n", - "Total params: 3,195,056\n", - "Trainable params: 3,191,728\n", - "Non-trainable params: 3,328\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "ICT_wPCGa897", - "outputId": "f8891daa-e442-4825-ff42-3da2fa1996da" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "tf.keras.utils.plot_model(model)" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "machine_shape": "hm", - "name": "unet_main.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/recognition/MySolution/utils.py b/recognition/MySolution/utils.py new file mode 100644 index 0000000000..e69de29bb2 From 150fef176126ca6fb63845c0ef7de904291d9d79 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 03:03:08 +1000 Subject: [PATCH 02/20] Created Vision Transformer. Training module needs to be made. --- recognition/MySolution/modules.py | 140 ++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index e69de29bb2..eaa18c9a08 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -0,0 +1,140 @@ +import numpy as np +import matplotlib.pyplot as plt +from tensorflow import keras +import tensorflow as tf +from tensorflow.keras import layers + +from train import * +from dataset import * + +def main(): + LoadData() # Loads all the given data + + x_train = np.load('X_train.npy') + + num_classes = 2 # AD or NC + input_shape = (240, 256, 1) + image_size = 250 + patch_size = 32 + num_patches = (image_size // patch_size) ** 2 + projection_dim = 64 # increase + num_heads = 8 # increase + transformer_units = [ + projection_dim * 3, # increase + projection_dim, + ] # size of the transformer layers + transformer_layers = 9 + mlp_head_units = [2048, 256] # Size of the dense layers of the final classifier + + plt.figure(figsize=(4, 4)) + image = x_train[np.random.choice(range(x_train.shape[0]))] + plt.imshow(image.astype("uint8"), cmap="gray") + plt.axis("off") + + resized_image = tf.image.resize( + tf.convert_to_tensor([image]), size=(image_size, image_size) + ) + plt.savefig("OG.jpg") + + patches = Patches(patch_size)(resized_image) + print(f"Image size: {image_size} X {image_size}", flush=True) + print(f"Patch size: {patch_size} X {patch_size}", flush=True) + print(f"Patches per image: {patches.shape[1]}", flush=True) + print(f"Elements per patch: {patches.shape[-1]}", flush=True) + + n = int(np.sqrt(patches.shape[1])) + plt.figure(figsize=(4, 4)) + + for i, patch in enumerate(patches[0]): + ax = plt.subplot(n, n, i + 1) + patch_img = tf.reshape(patch, (patch_size, patch_size, 1)) + plt.imshow(patch_img.numpy().astype("uint8"), cmap="gray") + plt.axis("off") + + plt.savefig("Test.jpg") + + + data_augmentation = keras.Sequential( + [ + layers.experimental.preprocessing.Normalization(), + layers.experimental.preprocessing.Resizing(image_size, image_size), + layers.experimental.preprocessing.RandomFlip("horizontal"), + layers.experimental.preprocessing.RandomRotation(factor=0.2), + layers.experimental.preprocessing.RandomZoom( + height_factor=0.2, width_factor=0.2 + ), + ], + name="data_augmentation" + ) + + data_augmentation.layers[0].adapt(x_train) + + inputs = layers.Input(shape=input_shape) + augmented = data_augmentation(inputs) + patches = Patches(patch_size)(augmented) + encoded_patches = PatchEncoder(num_patches, projection_dim)(patches) + + for _ in range(transformer_layers): + x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) + attention_output = layers.MultiHeadAttention( + num_heads=num_heads, key_dim=projection_dim, dropout=0.1 + )(x1, x1) + x2 = layers.Add()([attention_output, encoded_patches]) + x3 = layers.LayerNormalization(epsilon=1e-6)(x2) + x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1) + encoded_patches = layers.Add()([x3, x2]) + + representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) + representation = layers.Flatten()(representation) + representation = layers.Dropout(0.5)(representation) + + features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5) + + logits = layers.Dense(num_classes)(features) + + model = keras.Model(inputs=inputs, outputs=logits) + # model.summary() + Train(model=model) + +def mlp(x, hidden_units, dropout_rate): + for units in hidden_units: + x = layers.Dense(units, activation='relu')(x) + x = layers.Dropout(dropout_rate)(x) + return x + +class Patches(layers.Layer): + + def __init__(self, patch_size): + super(Patches, self).__init__() + self.patch_size = patch_size + + def call(self, images): + batch_size = tf.shape(images)[0] + patches = tf.image.extract_patches( + images=images, + sizes=[1, self.patch_size, self.patch_size, 1] , + strides=[1, self.patch_size, self.patch_size, 1], + rates=[1, 1, 1, 1], + padding="VALID", + ) + patch_dims = patches.shape[-1] + patches = tf.reshape(patches, [batch_size, -1, patch_dims]) + return patches + +class PatchEncoder(layers.Layer): + def __init__(self, num_patches, projection_dim): + super(PatchEncoder, self).__init__() + self.num_patches = num_patches + self.projection = layers.Dense(units=projection_dim) + self.position_embedding = layers.Embedding( + input_dim=num_patches, output_dim=projection_dim + ) + + def call(self, patch): + positions = tf.range(start=0, limit=self.num_patches, delta=1) + encoded = self.projection(patch) + self.position_embedding(positions) + return encoded + + +if __name__ == "__main__": + main() \ No newline at end of file From 84d8d183dddef6621d5c1ff118211c624c9552e2 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 03:04:03 +1000 Subject: [PATCH 03/20] Created training module. After running only 63% accuracy was achieved. --- recognition/MySolution/train.py | 69 +++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index e69de29bb2..4496e09cdc 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -0,0 +1,69 @@ +import numpy as np +from tensorflow import keras +import tensorflow as tf +import tensorflow_addons as tfa +import matplotlib.pyplot as plt + + + +class Train: + def __init__(self, model): + self.learning_rate = 0.001 + self.weight_decay = 0.0001 + self.batch_size = 64 + self.num_epochs = 50 + self.model = model + self.run_experiment(self.model) + + def run_experiment(self, model): + optimizer = tfa.optimizers.AdamW( + learning_rate = self.learning_rate, + weight_decay= self.weight_decay + ) + + model.compile( + optimizer=optimizer, + loss= tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + metrics = [ + keras.metrics.SparseCategoricalAccuracy(name="Accuracy"), + keras.metrics.SparseTopKCategoricalAccuracy(5, name="top-5-accuracy"), + ], + ) + + checkpoint_filepath = "/tmp/checkpoint" + checkpoint_callback = keras.callbacks.ModelCheckpoint( + checkpoint_filepath, + monitor = "val_Accuracy", + save_best_only = True, + save_weights_only = True + ) + + history = model.fit( + x=np.load('X_train.npy'), + y=np.load('y_train.npy'), + batch_size=self.batch_size, + epochs=self.num_epochs, + validation_split=0.1, + callbacks=[checkpoint_callback], + ) + + # os.listdir(checkpoint_filepath) + # model.load_weights(checkpoint_filepath) + # _, accuracy, top_5_accuracy = model.evaluate(np.load('X_train.npy'), np.load('y_train.npy')) + # print(f"Test Accuracy: {round(accuracy * 100, 2)}%") + # print(f"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%") + + plt.plot(history.history['Accuracy'], label='accuracy') + plt.plot(history.history['val_Accuracy'], label = 'val_accuracy') + plt.plot(history.history['loss'], label = 'loss') + + plt.xlabel('Epoch') + plt.ylabel('Accuracy') + plt.ylim([0.5, 2]) + plt.legend(loc='lower right') + + test_loss, test_acc, *is_anything_else_being_returned = model.evaluate(np.load('X_test.npy'), np.load('y_test.npy'), verbose=2) + print(f"test_loss: {test_loss}") + print(f"test_acc: {test_acc}") + print(f"is_anything_else_being_returned: {is_anything_else_being_returned}") + return history \ No newline at end of file From aff01003ecfd66e4057fd3f8351050baf1667ca2 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 03:06:54 +1000 Subject: [PATCH 04/20] Added new modifications to the number of heads, prejection_dim, and patch_size. Accuracy is yet to be tested. NOTE: more references n eed to be added to each code block to avoid plagarism penalty. --- recognition/MySolution/modules.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index eaa18c9a08..b4d2d872a3 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -14,13 +14,13 @@ def main(): num_classes = 2 # AD or NC input_shape = (240, 256, 1) - image_size = 250 - patch_size = 32 + image_size = 180 + patch_size = 16 num_patches = (image_size // patch_size) ** 2 - projection_dim = 64 # increase - num_heads = 8 # increase + projection_dim = 128 # increase + num_heads = 4 # increase transformer_units = [ - projection_dim * 3, # increase + projection_dim * 5, # increase projection_dim, ] # size of the transformer layers transformer_layers = 9 From 7cf443a033e8c237789079d00297ba1d0105d8d6 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 03:08:17 +1000 Subject: [PATCH 05/20] Increased the num_heads, and projection_dim in the model. Current val_accuracy is still aprroximately 0.72 --- recognition/MySolution/modules.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index b4d2d872a3..a94c23c635 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -14,13 +14,13 @@ def main(): num_classes = 2 # AD or NC input_shape = (240, 256, 1) - image_size = 180 - patch_size = 16 + image_size = 250 + patch_size = 32 num_patches = (image_size // patch_size) ** 2 projection_dim = 128 # increase - num_heads = 4 # increase + num_heads = 12 # increase transformer_units = [ - projection_dim * 5, # increase + projection_dim * 3, # increase projection_dim, ] # size of the transformer layers transformer_layers = 9 From 08ff4a12d1faea1865a9df59ce9d178609dcaf6e Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 03:09:04 +1000 Subject: [PATCH 06/20] Increased patch size to 32, increased image size to 250, and decreased epochs to 50. Average accuaracy is still 63% --- recognition/MySolution/modules.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index a94c23c635..8cb05ad724 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -14,8 +14,8 @@ def main(): num_classes = 2 # AD or NC input_shape = (240, 256, 1) - image_size = 250 - patch_size = 32 + image_size = 128 + patch_size = 18 num_patches = (image_size // patch_size) ** 2 projection_dim = 128 # increase num_heads = 12 # increase From 71e3355afc9d01ef3a905e13a2f0686b22a18f11 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 03:18:31 +1000 Subject: [PATCH 07/20] Changed loss function to binary cross entropy as it is either AD or NC. Also added a sigmoid layer at the end as suggested by tutor. --- recognition/MySolution/modules.py | 12 ++++++------ recognition/MySolution/train.py | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index 8cb05ad724..9157ed9d8b 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -8,14 +8,14 @@ from dataset import * def main(): - LoadData() # Loads all the given data + #LoadData() # Loads all the given data x_train = np.load('X_train.npy') - num_classes = 2 # AD or NC + num_classes = 1 # AD or NC input_shape = (240, 256, 1) - image_size = 128 - patch_size = 18 + image_size = 250 + patch_size = 32 num_patches = (image_size // patch_size) ** 2 projection_dim = 128 # increase num_heads = 12 # increase @@ -90,10 +90,10 @@ def main(): features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5) - logits = layers.Dense(num_classes)(features) + logits = layers.Dense(num_classes, activation="sigmoid")(features) model = keras.Model(inputs=inputs, outputs=logits) - # model.summary() + model.summary() Train(model=model) def mlp(x, hidden_units, dropout_rate): diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index 4496e09cdc..f1100be8eb 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -23,7 +23,7 @@ def run_experiment(self, model): model.compile( optimizer=optimizer, - loss= tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + loss= tf.keras.losses.BinaryCrossentropy(from_logits=False), metrics = [ keras.metrics.SparseCategoricalAccuracy(name="Accuracy"), keras.metrics.SparseTopKCategoricalAccuracy(5, name="top-5-accuracy"), From be3efaae85fe719083276596754b58b027b3fc1d Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 16:56:54 +1000 Subject: [PATCH 08/20] Added a softmax layer at the end instead of a sigmoid however, this does not improove the model. Model accuracy is stuck on 0.48. --- recognition/MySolution/dataset.py | 1 - recognition/MySolution/modules.py | 2 +- recognition/MySolution/train.py | 6 ------ 3 files changed, 1 insertion(+), 8 deletions(-) diff --git a/recognition/MySolution/dataset.py b/recognition/MySolution/dataset.py index bfadd3a692..bd94271705 100644 --- a/recognition/MySolution/dataset.py +++ b/recognition/MySolution/dataset.py @@ -1,4 +1,3 @@ - import numpy as np import os import cv2 diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index 9157ed9d8b..f8fdfd5410 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -90,7 +90,7 @@ def main(): features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5) - logits = layers.Dense(num_classes, activation="sigmoid")(features) + logits = layers.Dense(num_classes, activation="softmax")(features) model = keras.Model(inputs=inputs, outputs=logits) model.summary() diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index f1100be8eb..518822bc15 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -47,12 +47,6 @@ def run_experiment(self, model): callbacks=[checkpoint_callback], ) - # os.listdir(checkpoint_filepath) - # model.load_weights(checkpoint_filepath) - # _, accuracy, top_5_accuracy = model.evaluate(np.load('X_train.npy'), np.load('y_train.npy')) - # print(f"Test Accuracy: {round(accuracy * 100, 2)}%") - # print(f"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%") - plt.plot(history.history['Accuracy'], label='accuracy') plt.plot(history.history['val_Accuracy'], label = 'val_accuracy') plt.plot(history.history['loss'], label = 'loss') From 715a11dd0d48b0f0d9c078ca20907f18ac0e0fb8 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 18:57:41 +1000 Subject: [PATCH 09/20] Increased learning rate but this caused the loss to output nan meaning it is diverging to infinity. Further, accuracy remains on 0.48 --- recognition/MySolution/dataset.py | 43 +++---------------------------- recognition/MySolution/modules.py | 9 ++++--- recognition/MySolution/train.py | 9 +++---- 3 files changed, 13 insertions(+), 48 deletions(-) diff --git a/recognition/MySolution/dataset.py b/recognition/MySolution/dataset.py index bd94271705..e6ebc84f43 100644 --- a/recognition/MySolution/dataset.py +++ b/recognition/MySolution/dataset.py @@ -1,3 +1,5 @@ + +from cProfile import label import numpy as np import os import cv2 @@ -8,11 +10,10 @@ class LoadData: def __init__(self): self.load_training_data() - self.load_validation_data() self.load_testing_data() def load_training_data(self): - DATADIR = "ADNI_AD_NC_2D/AD_NC/train" + DATADIR = "/home/Student/s4644467/PatternFlow/recognition/AD_NC/train" CATEGORIES = ["AD", "NC"] count = 0 training_data = [] @@ -23,10 +24,6 @@ def load_training_data(self): img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE) training_data.append([img_arr, class_num]) count += 1 - if (count == 19368): - break - if (count == 19368): - break print("Loaded " + str(count) + " train images", flush=True) random.shuffle(training_data) @@ -43,40 +40,8 @@ def load_training_data(self): np.save("X_train.npy", X_train) np.save("y_train.npy", y_train) - - def load_validation_data(self): - DATADIR = "ADNI_AD_NC_2D/AD_NC/train" - CATEGORIES = ["AD", "NC"] - count = 0 - training_data = [] - for category in CATEGORIES: - path = os.path.join(DATADIR, category) - class_num = CATEGORIES.index(category) - for img in os.listdir(path): - count += 1 - if (count <= 19368): - continue - img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE) - training_data.append([img_arr, class_num]) - - print("Loaded " + str(count - 19368) + " validation images", flush=True) - random.shuffle(training_data) - - X = [] - y = [] - - for features, label in training_data: - X.append(features) - y.append(label) - - X = np.array(X).reshape(-1, 240, 256, 1) - print(X.shape) - - np.save("X_validate.npy", X) - np.save("y_validate.npy", y) - def load_testing_data(self): - DATADIR = "ADNI_AD_NC_2D/AD_NC/test" + DATADIR = "/home/Student/s4644467/PatternFlow/recognition/AD_NC/test" CATEGORIES = ["AD", "NC"] count = 0 training_data = [] diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index f8fdfd5410..d5f9c4706b 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -1,3 +1,4 @@ +import re import numpy as np import matplotlib.pyplot as plt from tensorflow import keras @@ -17,8 +18,8 @@ def main(): image_size = 250 patch_size = 32 num_patches = (image_size // patch_size) ** 2 - projection_dim = 128 # increase - num_heads = 12 # increase + projection_dim = 64 # increase + num_heads = 8 # increase transformer_units = [ projection_dim * 3, # increase projection_dim, @@ -90,10 +91,10 @@ def main(): features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5) - logits = layers.Dense(num_classes, activation="softmax")(features) + logits = layers.Dense(num_classes, activation="sigmoid")(features) model = keras.Model(inputs=inputs, outputs=logits) - model.summary() + # model.summary() Train(model=model) def mlp(x, hidden_units, dropout_rate): diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index 518822bc15..480fba7a6e 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -8,8 +8,8 @@ class Train: def __init__(self, model): - self.learning_rate = 0.001 - self.weight_decay = 0.0001 + self.learning_rate = 0.01 + self.weight_decay = 0.000001 self.batch_size = 64 self.num_epochs = 50 self.model = model @@ -23,10 +23,9 @@ def run_experiment(self, model): model.compile( optimizer=optimizer, - loss= tf.keras.losses.BinaryCrossentropy(from_logits=False), + loss= tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics = [ - keras.metrics.SparseCategoricalAccuracy(name="Accuracy"), - keras.metrics.SparseTopKCategoricalAccuracy(5, name="top-5-accuracy"), + keras.metrics.BinaryAccuracy(name="Accuracy"), ], ) From 5efdf72b45db763874a91556c35ad3e628f332ed Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 22:49:01 +1000 Subject: [PATCH 10/20] Added comments for dataset.py --- recognition/MySolution/dataset.py | 53 +++++++++++++++++++++++-------- 1 file changed, 40 insertions(+), 13 deletions(-) diff --git a/recognition/MySolution/dataset.py b/recognition/MySolution/dataset.py index e6ebc84f43..862cc48b76 100644 --- a/recognition/MySolution/dataset.py +++ b/recognition/MySolution/dataset.py @@ -1,22 +1,34 @@ from cProfile import label +import tensorflow as tf import numpy as np import os import cv2 import random - +""" +Class for loading data. This class will load all image data +and store it an a npy array for later use. +""" class LoadData: + """ + constructor of the class that calls the train and validate methods. + These methods will load all image data as train and test sets + """ def __init__(self): - self.load_training_data() - self.load_testing_data() + self.load_training_data() # Calls method to load training data + self.load_testing_data() # Calls method to test training data + """ + Method for loading training data set. It loads all images from a predefined + directory and loads and stores all image data as a npy array. + """ def load_training_data(self): DATADIR = "/home/Student/s4644467/PatternFlow/recognition/AD_NC/train" - CATEGORIES = ["AD", "NC"] - count = 0 - training_data = [] + CATEGORIES = ["AD", "NC"] # Looping through all the categories + count = 0 # keeps count of how many images are loaded + training_data = [] # list containing all the training data for category in CATEGORIES: path = os.path.join(DATADIR, category) class_num = CATEGORIES.index(category) @@ -24,27 +36,37 @@ def load_training_data(self): img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE) training_data.append([img_arr, class_num]) count += 1 + if (count % 100 == 0): # Keeps track of how many images are loaded + print(f"Loaded {count} train images", flush=True) print("Loaded " + str(count) + " train images", flush=True) - random.shuffle(training_data) + random.shuffle(training_data) # Shuffles image data X_train = [] y_train = [] + # Appends image to their labels for features, label in training_data: X_train.append(features) y_train.append(label) + # Reshapes to given image size X_train = np.array(X_train).reshape(-1, 240, 256, 1) print(X_train.shape) + # Saves image data as numpy arrays to access later np.save("X_train.npy", X_train) np.save("y_train.npy", y_train) + + """ + Method for loading test data set. It loads all images from a predefined + directory and loads and stores all image data as a npy array. + """ def load_testing_data(self): DATADIR = "/home/Student/s4644467/PatternFlow/recognition/AD_NC/test" - CATEGORIES = ["AD", "NC"] - count = 0 - training_data = [] + CATEGORIES = ["AD", "NC"] # Looping through all the categories + count = 0 # keeps count of how many images are loaded + training_data = [] # list containing all the training data for category in CATEGORIES: path = os.path.join(DATADIR, category) class_num = CATEGORIES.index(category) @@ -52,19 +74,24 @@ def load_testing_data(self): img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE) training_data.append([img_arr, class_num]) count += 1 + if (count % 100 == 0): # Keeps track of how many images are loaded + print(f"Loaded {count} test images", flush=True) print("Loaded " + str(count) + " test images", flush=True) - random.shuffle(training_data) + random.shuffle(training_data) # Shuffles image data X_train = [] y_train = [] - + + # Appends image to their labels for features, label in training_data: X_train.append(features) y_train.append(label) - + + # Reshapes to given image size X_train = np.array(X_train).reshape(-1, 240, 256, 1) print(X_train.shape) + # Saves image data as numpy arrays to access later np.save("X_test.npy", X_train) np.save("y_test.npy", y_train) \ No newline at end of file From a6f777872d93b1415e712eaa45701badf07eed0f Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 23:16:00 +1000 Subject: [PATCH 11/20] Added comments to modules.py --- recognition/MySolution/modules.py | 235 +++++++++++++++++------------- 1 file changed, 136 insertions(+), 99 deletions(-) diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index d5f9c4706b..d2d9d41a2b 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -1,114 +1,142 @@ -import re import numpy as np import matplotlib.pyplot as plt from tensorflow import keras import tensorflow as tf from tensorflow.keras import layers - from train import * from dataset import * -def main(): - #LoadData() # Loads all the given data - - x_train = np.load('X_train.npy') - - num_classes = 1 # AD or NC - input_shape = (240, 256, 1) - image_size = 250 - patch_size = 32 - num_patches = (image_size // patch_size) ** 2 - projection_dim = 64 # increase - num_heads = 8 # increase - transformer_units = [ - projection_dim * 3, # increase - projection_dim, - ] # size of the transformer layers - transformer_layers = 9 - mlp_head_units = [2048, 256] # Size of the dense layers of the final classifier - - plt.figure(figsize=(4, 4)) - image = x_train[np.random.choice(range(x_train.shape[0]))] - plt.imshow(image.astype("uint8"), cmap="gray") - plt.axis("off") - - resized_image = tf.image.resize( - tf.convert_to_tensor([image]), size=(image_size, image_size) - ) - plt.savefig("OG.jpg") - - patches = Patches(patch_size)(resized_image) - print(f"Image size: {image_size} X {image_size}", flush=True) - print(f"Patch size: {patch_size} X {patch_size}", flush=True) - print(f"Patches per image: {patches.shape[1]}", flush=True) - print(f"Elements per patch: {patches.shape[-1]}", flush=True) - - n = int(np.sqrt(patches.shape[1])) - plt.figure(figsize=(4, 4)) - - for i, patch in enumerate(patches[0]): - ax = plt.subplot(n, n, i + 1) - patch_img = tf.reshape(patch, (patch_size, patch_size, 1)) - plt.imshow(patch_img.numpy().astype("uint8"), cmap="gray") +""" +Vision transformer class that contains all the model data +""" +class VIT: + """ + Constructor used to call main + """ + def __init__(self): + self.main() + + def main(self): + LoadData() # Loads all the given data + + x_train = np.load('X_train.npy') # Loads x_train data + + # All the parameters for the Vision Transformer + num_classes = 1 # AD or NC + input_shape = (240, 256, 1) + image_size = 250 + patch_size = 32 + num_patches = (image_size // patch_size) ** 2 + projection_dim = 64 + num_heads = 8 + transformer_units = [ + projection_dim * 3, + projection_dim, + ] + # size of the transformer layers + transformer_layers = 9 + # Size of the dense layers of the final classifier + mlp_head_units = [2048, 256] + + #Plots all the images + plt.figure(figsize=(4, 4)) + image = x_train[np.random.choice(range(x_train.shape[0]))] + plt.imshow(image.astype("uint8"), cmap="gray") plt.axis("off") - plt.savefig("Test.jpg") - - - data_augmentation = keras.Sequential( - [ - layers.experimental.preprocessing.Normalization(), - layers.experimental.preprocessing.Resizing(image_size, image_size), - layers.experimental.preprocessing.RandomFlip("horizontal"), - layers.experimental.preprocessing.RandomRotation(factor=0.2), - layers.experimental.preprocessing.RandomZoom( - height_factor=0.2, width_factor=0.2 - ), - ], - name="data_augmentation" - ) - - data_augmentation.layers[0].adapt(x_train) - - inputs = layers.Input(shape=input_shape) - augmented = data_augmentation(inputs) - patches = Patches(patch_size)(augmented) - encoded_patches = PatchEncoder(num_patches, projection_dim)(patches) - - for _ in range(transformer_layers): - x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) - attention_output = layers.MultiHeadAttention( - num_heads=num_heads, key_dim=projection_dim, dropout=0.1 - )(x1, x1) - x2 = layers.Add()([attention_output, encoded_patches]) - x3 = layers.LayerNormalization(epsilon=1e-6)(x2) - x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1) - encoded_patches = layers.Add()([x3, x2]) - - representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) - representation = layers.Flatten()(representation) - representation = layers.Dropout(0.5)(representation) - - features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5) - - logits = layers.Dense(num_classes, activation="sigmoid")(features) - - model = keras.Model(inputs=inputs, outputs=logits) - # model.summary() - Train(model=model) - -def mlp(x, hidden_units, dropout_rate): - for units in hidden_units: - x = layers.Dense(units, activation='relu')(x) - x = layers.Dropout(dropout_rate)(x) - return x - + resized_image = tf.image.resize( + tf.convert_to_tensor([image]), size=(image_size, image_size) + ) + plt.savefig("OG.jpg") + + # Prints out all the data for the images + patches = Patches(patch_size)(resized_image) + print(f"Image size: {image_size} X {image_size}", flush=True) + print(f"Patch size: {patch_size} X {patch_size}", flush=True) + print(f"Patches per image: {patches.shape[1]}", flush=True) + print(f"Elements per patch: {patches.shape[-1]}", flush=True) + + n = int(np.sqrt(patches.shape[1])) + plt.figure(figsize=(4, 4)) + + for i, patch in enumerate(patches[0]): + ax = plt.subplot(n, n, i + 1) + patch_img = tf.reshape(patch, (patch_size, patch_size, 1)) + plt.imshow(patch_img.numpy().astype("uint8"), cmap="gray") + plt.axis("off") + + plt.savefig("Test.jpg") + + # Data Augmentation used to improove model + data_augmentation = keras.Sequential( + [ + layers.experimental.preprocessing.Normalization(), + layers.experimental.preprocessing.Resizing(image_size, image_size), + layers.experimental.preprocessing.RandomFlip("horizontal"), + layers.experimental.preprocessing.RandomRotation(factor=0.2), + layers.experimental.preprocessing.RandomZoom( + height_factor=0.2, width_factor=0.2 + ), + ], + name="data_augmentation" + ) + + data_augmentation.layers[0].adapt(x_train) + inputs = layers.Input(shape=input_shape) + augmented = data_augmentation(inputs) + patches = Patches(patch_size)(augmented) # Creates patches + encoded_patches = PatchEncoder(num_patches, projection_dim)(patches) # Applies Patch Encoder + + # Loops through all transformer layers + for _ in range(transformer_layers): + x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) + attention_output = layers.MultiHeadAttention( + num_heads=num_heads, key_dim=projection_dim, dropout=0.1 + )(x1, x1) + x2 = layers.Add()([attention_output, encoded_patches]) + x3 = layers.LayerNormalization(epsilon=1e-6)(x2) + x3 = self.mlp(x3, hidden_units=transformer_units, dropout_rate=0.1) + encoded_patches = layers.Add()([x3, x2]) + + # Adds additional layers + representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches) + representation = layers.Flatten()(representation) + representation = layers.Dropout(0.5)(representation) + + # Calls the Multi layer Perceptron + features = self.mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5) + # Final layer of the model. Uses Sigmoid activation as it works the best (based on trial and error) + logits = layers.Dense(num_classes, activation="sigmoid")(features) + + model = keras.Model(inputs=inputs, outputs=logits) + model.summary() # Outputs a model Summary + Train(model=model) # Trains the model + + """ + Function for the multi layer perceptron. + """ + def mlp(self, x, hidden_units, dropout_rate): + for units in hidden_units: + x = layers.Dense(units, activation=tf.nn.gelu)(x) + x = layers.Dropout(dropout_rate)(x) + return x + + +""" +Patches class used for creating patches +""" class Patches(layers.Layer): + """ + Constructor for the Patch class + """ def __init__(self, patch_size): super(Patches, self).__init__() self.patch_size = patch_size - + + """ + Creates the patches and returns it + """ def call(self, images): batch_size = tf.shape(images)[0] patches = tf.image.extract_patches( @@ -122,7 +150,15 @@ def call(self, images): patches = tf.reshape(patches, [batch_size, -1, patch_dims]) return patches + +""" +Patch Encoding class used to encode Patches +""" class PatchEncoder(layers.Layer): + + """ + Constructor for the Patch encoder class + """ def __init__(self, num_patches, projection_dim): super(PatchEncoder, self).__init__() self.num_patches = num_patches @@ -131,11 +167,12 @@ def __init__(self, num_patches, projection_dim): input_dim=num_patches, output_dim=projection_dim ) + """ + Returns encoded Patch + """ def call(self, patch): positions = tf.range(start=0, limit=self.num_patches, delta=1) encoded = self.projection(patch) + self.position_embedding(positions) return encoded - -if __name__ == "__main__": - main() \ No newline at end of file +VIT() # Calls the Vision Transformer class to begin running the model \ No newline at end of file From f63c95b12aad81e73d1e174a0bfbad6a96aee02c Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 23:21:25 +1000 Subject: [PATCH 12/20] Added comments to train.py --- recognition/MySolution/train.py | 60 +++++++++++++++++++++------------ 1 file changed, 38 insertions(+), 22 deletions(-) diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index 480fba7a6e..49cc734347 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -5,55 +5,71 @@ import matplotlib.pyplot as plt - +""" +Class for training the model +""" class Train: + + """ + Constructor for the Training class + Initialises all the parameters to be used for training + """ def __init__(self, model): - self.learning_rate = 0.01 - self.weight_decay = 0.000001 + # parameters to be used for training + self.learning_rate = 0.001 + self.weight_decay = 0.0001 self.batch_size = 64 self.num_epochs = 50 self.model = model - self.run_experiment(self.model) + self.train(self.model) + + """ + Method used to train the model + """ + def train(self, model): - def run_experiment(self, model): + # Defines the optimizer to be used optimizer = tfa.optimizers.AdamW( learning_rate = self.learning_rate, weight_decay= self.weight_decay ) + # Compiles the model model.compile( optimizer=optimizer, - loss= tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + loss= tf.keras.losses.BinaryCrossentropy(from_logits=False), # Uses BinaryCrossentropy as there are only 2 classes metrics = [ keras.metrics.BinaryAccuracy(name="Accuracy"), ], ) - checkpoint_filepath = "/tmp/checkpoint" - checkpoint_callback = keras.callbacks.ModelCheckpoint( - checkpoint_filepath, - monitor = "val_Accuracy", - save_best_only = True, - save_weights_only = True - ) - + # Trains the model history = model.fit( x=np.load('X_train.npy'), y=np.load('y_train.npy'), batch_size=self.batch_size, epochs=self.num_epochs, validation_split=0.1, - callbacks=[checkpoint_callback], ) - plt.plot(history.history['Accuracy'], label='accuracy') - plt.plot(history.history['val_Accuracy'], label = 'val_accuracy') - plt.plot(history.history['loss'], label = 'loss') - + # Plots the results + xs = range(self.num_epochs) + plt.figure() + plt.plot(xs, history.history["loss"], label="loss") + plt.plot(xs, history.history["val_loss"], label="val_loss") plt.xlabel('Epoch') - plt.ylabel('Accuracy') - plt.ylim([0.5, 2]) - plt.legend(loc='lower right') + plt.ylabel('loss') + plt.savefig("Final.jpg") + + + # plt.plot(history.history['Accuracy'], label='accuracy') + # plt.plot(history.history['val_Accuracy'], label = 'val_accuracy') + # plt.plot(history.history['loss'], label = 'loss') + + # plt.xlabel('Epoch') + # plt.ylabel('Accuracy') + # plt.legend(loc='lower right') + # plt.savefig("Final.jpg") test_loss, test_acc, *is_anything_else_being_returned = model.evaluate(np.load('X_test.npy'), np.load('y_test.npy'), verbose=2) print(f"test_loss: {test_loss}") From 735fb72d4f6b387c71dbdb5d3868fa39478b5d8a Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 23:33:59 +1000 Subject: [PATCH 13/20] Added more comments to train.py and predict.py --- recognition/MySolution/predict.py | 20 +++++++++++++++++++ recognition/MySolution/train.py | 33 +++++++++++++++++-------------- 2 files changed, 38 insertions(+), 15 deletions(-) diff --git a/recognition/MySolution/predict.py b/recognition/MySolution/predict.py index e69de29bb2..7cbe50225b 100644 --- a/recognition/MySolution/predict.py +++ b/recognition/MySolution/predict.py @@ -0,0 +1,20 @@ +import numpy as np + + +""" +Outputs the overall accuracy of the data based on the model trained +""" +class Predict: + """ + Constructor of Predict class used to call plot_data() + """ + def __init__(self, model): + self.plot_data(model) + + """ + Uses the test data to return the accuracy of the model. + """ + def plot_data(self, model): + test_loss, test_acc, *is_anything_else_being_returned = model.evaluate(np.load('X_test.npy'), np.load('y_test.npy'), verbose=2) + print(f"test_loss: {test_loss}") + print(f"test_acc: {test_acc}") diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index 49cc734347..787d2f5327 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -3,6 +3,7 @@ import tensorflow as tf import tensorflow_addons as tfa import matplotlib.pyplot as plt +from predict import * """ @@ -19,7 +20,7 @@ def __init__(self, model): self.learning_rate = 0.001 self.weight_decay = 0.0001 self.batch_size = 64 - self.num_epochs = 50 + self.num_epochs = 10 self.model = model self.train(self.model) @@ -59,20 +60,22 @@ def train(self, model): plt.plot(xs, history.history["val_loss"], label="val_loss") plt.xlabel('Epoch') plt.ylabel('loss') - plt.savefig("Final.jpg") + plt.savefig("Loss.jpg") + plt.imshow() + plt.show() + xs = range(self.num_epochs) + plt.figure() + plt.plot(xs, history.history["Accuracy"], label="Accuracy") + plt.plot(xs, history.history["val_Accuracy"], label="val_Accuracy") + plt.xlabel('Epoch') + plt.ylabel('accuracy') + plt.savefig("Accuracy.jpg") + plt.imshow() + plt.show() - # plt.plot(history.history['Accuracy'], label='accuracy') - # plt.plot(history.history['val_Accuracy'], label = 'val_accuracy') - # plt.plot(history.history['loss'], label = 'loss') - - # plt.xlabel('Epoch') - # plt.ylabel('Accuracy') - # plt.legend(loc='lower right') - # plt.savefig("Final.jpg") - - test_loss, test_acc, *is_anything_else_being_returned = model.evaluate(np.load('X_test.npy'), np.load('y_test.npy'), verbose=2) - print(f"test_loss: {test_loss}") - print(f"test_acc: {test_acc}") - print(f"is_anything_else_being_returned: {is_anything_else_being_returned}") + Predict(model) + # test_loss, test_acc, *is_anything_else_being_returned = model.evaluate(np.load('X_test.npy'), np.load('y_test.npy'), verbose=2) + # print(f"test_loss: {test_loss}") + # print(f"test_acc: {test_acc}") return history \ No newline at end of file From 289522d54c35db206c3c7f2ee0b4a522d53e5cd4 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 23:45:42 +1000 Subject: [PATCH 14/20] Added references to all the code used. Also added a ReadMe file --- recognition/MySolution/README.md | 6 ++++++ recognition/MySolution/dataset.py | 6 +++++- recognition/MySolution/modules.py | 6 ++++++ recognition/MySolution/predict.py | 2 +- recognition/MySolution/train.py | 5 +++++ recognition/MySolution/utils.py | 0 6 files changed, 23 insertions(+), 2 deletions(-) create mode 100644 recognition/MySolution/README.md delete mode 100644 recognition/MySolution/utils.py diff --git a/recognition/MySolution/README.md b/recognition/MySolution/README.md new file mode 100644 index 0000000000..c1ab252200 --- /dev/null +++ b/recognition/MySolution/README.md @@ -0,0 +1,6 @@ +This is an example of a Vision Transformer used to classify a person who has Alzhimers or not + +Sources used for this are +https://www.youtube.com/watch?v=j-3vuBynnOE +https://www.youtube.com/watch?v=i2_zJ0ANrw0 +https://keras.io/examples/vision/image_classification_with_vision_transformer/ \ No newline at end of file diff --git a/recognition/MySolution/dataset.py b/recognition/MySolution/dataset.py index 862cc48b76..7e750d8f2c 100644 --- a/recognition/MySolution/dataset.py +++ b/recognition/MySolution/dataset.py @@ -1,11 +1,15 @@ -from cProfile import label import tensorflow as tf import numpy as np import os import cv2 import random +""" +Note: All the code used in this file has been inspired by +https://www.youtube.com/watch?v=j-3vuBynnOE +""" + """ Class for loading data. This class will load all image data and store it an a npy array for later use. diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index d2d9d41a2b..6ffb6c8d33 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -6,6 +6,12 @@ from train import * from dataset import * + +""" +Note: All the code used in this file has been inspired by +https://keras.io/examples/vision/image_classification_with_vision_transformer/ +""" + """ Vision transformer class that contains all the model data """ diff --git a/recognition/MySolution/predict.py b/recognition/MySolution/predict.py index 7cbe50225b..b68877761f 100644 --- a/recognition/MySolution/predict.py +++ b/recognition/MySolution/predict.py @@ -15,6 +15,6 @@ def __init__(self, model): Uses the test data to return the accuracy of the model. """ def plot_data(self, model): - test_loss, test_acc, *is_anything_else_being_returned = model.evaluate(np.load('X_test.npy'), np.load('y_test.npy'), verbose=2) + test_loss, test_acc, *anything_else_being_returned = model.evaluate(np.load('X_test.npy'), np.load('y_test.npy'), verbose=2) print(f"test_loss: {test_loss}") print(f"test_acc: {test_acc}") diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index 787d2f5327..f5bab316a3 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -6,6 +6,11 @@ from predict import * +""" +Note: All the code used in this file has been inspired by +https://keras.io/examples/vision/image_classification_with_vision_transformer/ +""" + """ Class for training the model """ diff --git a/recognition/MySolution/utils.py b/recognition/MySolution/utils.py deleted file mode 100644 index e69de29bb2..0000000000 From c30157186bf6a3f0fd227408e185e90b1bced4b2 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 23:48:28 +1000 Subject: [PATCH 15/20] Test commit --- recognition/MySolution/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/recognition/MySolution/README.md b/recognition/MySolution/README.md index c1ab252200..eb2feeff03 100644 --- a/recognition/MySolution/README.md +++ b/recognition/MySolution/README.md @@ -3,4 +3,6 @@ This is an example of a Vision Transformer used to classify a person who has Alz Sources used for this are https://www.youtube.com/watch?v=j-3vuBynnOE https://www.youtube.com/watch?v=i2_zJ0ANrw0 -https://keras.io/examples/vision/image_classification_with_vision_transformer/ \ No newline at end of file +https://keras.io/examples/vision/image_classification_with_vision_transformer/ + +Changes Done: \ No newline at end of file From 53227a1d47f5eb422e6c03e303dc4758a07325b2 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 23:50:17 +1000 Subject: [PATCH 16/20] Removed commented code from train.py --- recognition/MySolution/README.md | 2 +- recognition/MySolution/train.py | 3 --- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/recognition/MySolution/README.md b/recognition/MySolution/README.md index eb2feeff03..79b83ff27d 100644 --- a/recognition/MySolution/README.md +++ b/recognition/MySolution/README.md @@ -5,4 +5,4 @@ https://www.youtube.com/watch?v=j-3vuBynnOE https://www.youtube.com/watch?v=i2_zJ0ANrw0 https://keras.io/examples/vision/image_classification_with_vision_transformer/ -Changes Done: \ No newline at end of file +Changes Done: diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index f5bab316a3..21b35e5d21 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -80,7 +80,4 @@ def train(self, model): plt.show() Predict(model) - # test_loss, test_acc, *is_anything_else_being_returned = model.evaluate(np.load('X_test.npy'), np.load('y_test.npy'), verbose=2) - # print(f"test_loss: {test_loss}") - # print(f"test_acc: {test_acc}") return history \ No newline at end of file From 8f102f3586300e953808bfa5d4bffe59f3bbd3df Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Fri, 21 Oct 2022 23:52:05 +1000 Subject: [PATCH 17/20] Removed more commented code from train.py --- recognition/MySolution/train.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index 21b35e5d21..2f79bb2686 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -66,7 +66,6 @@ def train(self, model): plt.xlabel('Epoch') plt.ylabel('loss') plt.savefig("Loss.jpg") - plt.imshow() plt.show() xs = range(self.num_epochs) @@ -76,8 +75,7 @@ def train(self, model): plt.xlabel('Epoch') plt.ylabel('accuracy') plt.savefig("Accuracy.jpg") - plt.imshow() plt.show() - Predict(model) + Predict(model) # Calls the predict class to evaluate the model return history \ No newline at end of file From 41d528e3ff0068b374f8fc21f8e1f4cf2692bdc3 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Sun, 23 Oct 2022 22:27:58 +1000 Subject: [PATCH 18/20] Accidentally forgot to change variables epochs and forgot to revert changes for adding legend in the plot. Also edited spelling error in comment for modules.py --- recognition/MySolution/README.md | 8 -------- recognition/MySolution/modules.py | 2 +- recognition/MySolution/train.py | 2 +- 3 files changed, 2 insertions(+), 10 deletions(-) delete mode 100644 recognition/MySolution/README.md diff --git a/recognition/MySolution/README.md b/recognition/MySolution/README.md deleted file mode 100644 index 79b83ff27d..0000000000 --- a/recognition/MySolution/README.md +++ /dev/null @@ -1,8 +0,0 @@ -This is an example of a Vision Transformer used to classify a person who has Alzhimers or not - -Sources used for this are -https://www.youtube.com/watch?v=j-3vuBynnOE -https://www.youtube.com/watch?v=i2_zJ0ANrw0 -https://keras.io/examples/vision/image_classification_with_vision_transformer/ - -Changes Done: diff --git a/recognition/MySolution/modules.py b/recognition/MySolution/modules.py index 6ffb6c8d33..adb5249cfc 100644 --- a/recognition/MySolution/modules.py +++ b/recognition/MySolution/modules.py @@ -73,7 +73,7 @@ def main(self): plt.savefig("Test.jpg") - # Data Augmentation used to improove model + # Data Augmentation used to improve model data_augmentation = keras.Sequential( [ layers.experimental.preprocessing.Normalization(), diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index 2f79bb2686..048b104bd4 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -25,7 +25,7 @@ def __init__(self, model): self.learning_rate = 0.001 self.weight_decay = 0.0001 self.batch_size = 64 - self.num_epochs = 10 + self.num_epochs = 50 self.model = model self.train(self.model) From 29152a94e530d1959211364edf4b245213d9a9f3 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Sun, 23 Oct 2022 22:33:13 +1000 Subject: [PATCH 19/20] forgot to add plt.legend(loc='lower right') into the code. I had it initially on my cluster copy and forgot to copy it over --- recognition/MySolution/train.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/recognition/MySolution/train.py b/recognition/MySolution/train.py index 048b104bd4..a38e6fc248 100644 --- a/recognition/MySolution/train.py +++ b/recognition/MySolution/train.py @@ -65,6 +65,7 @@ def train(self, model): plt.plot(xs, history.history["val_loss"], label="val_loss") plt.xlabel('Epoch') plt.ylabel('loss') + plt.legend(loc='lower right') plt.savefig("Loss.jpg") plt.show() @@ -74,6 +75,7 @@ def train(self, model): plt.plot(xs, history.history["val_Accuracy"], label="val_Accuracy") plt.xlabel('Epoch') plt.ylabel('accuracy') + plt.legend(loc='lower right') plt.savefig("Accuracy.jpg") plt.show() From 6527008b90020927c9dac9a92db12c97692afed3 Mon Sep 17 00:00:00 2001 From: pranaypremdas Date: Thu, 10 Nov 2022 02:03:29 +1000 Subject: [PATCH 20/20] Added readme file --- recognition/MySolution/Accuracy.jpg | Bin 0 -> 23625 bytes recognition/MySolution/Loss.jpg | Bin 0 -> 21857 bytes recognition/MySolution/OG.jpg | Bin 0 -> 10769 bytes recognition/MySolution/Original.jpg | Bin 0 -> 10476 bytes recognition/MySolution/Patches.jpg | Bin 0 -> 20645 bytes recognition/MySolution/README.md | 146 +++++++++++++++++++++ recognition/MySolution/README.pdf | Bin 0 -> 904701 bytes recognition/MySolution/Scrambled.png | Bin 0 -> 174051 bytes recognition/MySolution/Test.jpg | Bin 0 -> 20594 bytes recognition/MySolution/TestingAccuracy.png | Bin 0 -> 35704 bytes recognition/MySolution/log 1.png | Bin 0 -> 268302 bytes recognition/MySolution/log 2.png | Bin 0 -> 276203 bytes recognition/MySolution/log 3.png | Bin 0 -> 284650 bytes 13 files changed, 146 insertions(+) create mode 100644 recognition/MySolution/Accuracy.jpg create mode 100644 recognition/MySolution/Loss.jpg create mode 100644 recognition/MySolution/OG.jpg create mode 100644 recognition/MySolution/Original.jpg create mode 100644 recognition/MySolution/Patches.jpg 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Vision transformers are an image classification model that ustilises a transformer type architecture on patches of an image. This is achieved in 4 main steps [2]. +

+## How the model works + +### Step 1: Creating image patches + + +
+
+ +In this step images that are created into patches and flattened in a linear sequence (Positional Embedding). By doing so the transformer is able to remember the ordering of each patch. This is important because an image could have the same patches but they aren't in the right order. An example of this is shown below. + +
+ + +
+ +### Step 2: Patch Encoding +Patch encoding can be done in various different ways depending on the architecture of the transformer. In this case a multilayer perceptron is used along with a sigmoid classification layer at the end. These help the vision transformer do the image classification (AD or NC). +

+ +### Step 3: Training +In this step the model created from the mlp and transformer layers is trained till a satisfactory va_accuracy is reached. A AdamW optimizer alongside with a BinaryCrossEntropy loss function is used in this process to improve the model. +

+### Step 4: Testing +In this final step the data collected from the training model is plotted into loss and accuracy graphs. In addition, the model is finally tested on testing data which outputs the final accuracy of it + +
+
+ +# How the model performs +Currently, the model performs approximately at 65% accuracy with a loss and accuracy graph as shown below. +
+
+ + + +
+
+ + + + +
+
+ +While such a low accuracy is not ideal, the following methods were done in an attempt to improve the accuracy + +
+
+ +## Method 1: Increasing Epochs and reducing image size +The cluster provided to train has a limited gpu allocation. Therefore, if the training model used surpasses that limit, the training will immediately halt. This was one of the biggest limitations of the task. As such to avoid exceeding this limit one startegy used was to decrease the image size. By doing so the quality of the image is lowered thereby making it easier for it to train the model. However, by doing so one limitation of this is more epochs are required to get higher val_accuracy's. This strategy was implimented using two different loss functions SparseCategoricalCrossentropy and BinaryCrossentropy. By doing so I aimed to figure out which loss function worked best. Overall, no drastic difffernces were observed both loss functions outputed an accuracy of 63% +- 1%. + +
+ +## Method 2: Decreasing Epochs and increasing image size +Similar to the method earlier in this method the image size is increased by doing so the model is able to capture more detail about each classification. However, the limitation of using this method is that the number of epochs the cluster can run is reduced (due to the higher image resolution). This strategy was also implimented using two different loss functions SparseCategoricalCrossentropy and BinaryCrossentropy. Overall, no drastic difffernces were observed both loss functions outputed an accuracy of 63% +- 1%. + +
+ +## Method 3: Normalizing the data +In this method each image that is passed through the transformer is normalised. By doing so, I aimed to increase the accuracy of the model [4]. By implimenting this technique no observable changes in accuracy was made. This strategy was also implimented using two different loss functions SparseCategoricalCrossentropy and BinaryCrossentropy. The final test accuracy after doing so was 63% +- 1% in both cases. + +
+ +## Method 4: Adding a softmax/sigmoid activation in the last layer +In this method a softmax layer was added in as it was what Source [1] reccomended using for the final layer. By doing so the models accuracy hit a maximum of 48%. Therefore, this layer was not used in the final model. A sigmoid layer as suggested by a tutor was later used and did improove the accuracy by 2% resulting in an average accuracy of 65%. + +
+ +## Method 5: Adjusting the parameters of the vision transformer: +The vision transformer has other parameter that can be used to fine tune the model such as + +- Patch size +- Projection dimensions +- Number of heads +- Transformer units + +According to Source [2] by increasing these values it is possible to increase the accuracy of the transformer. Therefore, they were increased/decreased in different variations. However, no combination that I have tried so far has resulted in a better testing accuracy. + +
+ +## Method 6: Adding a convolusion layer in the multi layer perceptron: +This method did not have any reasoning behind it. It was simply a random idea that I decided to impliment as a last resort. However, no improovements in the model were observed. As such I did not use it + +
+ +# Preprocessing and testing/training/validation dataset + +## testing/training/validation +For the testing and validation a 90:10 split was used as it was a commonly used splt in most datasets for machine learning [5]. The testing set was provided seperately in the data set therefore, no additinal splits were required for it. +
+ +## Pre-processing +To pre-process the data a data augmentation method was used as it was present in source [2] and also reccomended by sources online such as [6] and [7]. By implimenting this method variations of the images passed through are created. This therefore, increase the variety of different images (usefull when there is not a lot of training data given). In this preprocessing model the images undergo a normalaization, Resizing, RandomFlip, RandomRotation, and RandomZoom. + +While not a lot of research was done on this section from my part I believe that to improove the model further reasearch could be done in this space to potentially improve the accuracy of the vision transformer. + +
+ +# Dependencies +To run this model a few dependencies need to be used namely +- tensorflow +- numpy +- os +- cv2 (install opencv) +- random +- matplotlib +- keras/keras.layers +- matplotlib.pyplot +- tensorflow_addons + +Further to run the model the ADNI data set needs to be downloaded from https://adni.loni.usc.edu/. After downloading the dataset, load it into the solution folder and ensure that the path DATADIR in dataset.py is updated for both training and testing functions. + +After doing so the command: srun -p test -G1 python3 modules.py can be used to run the model. + +
+ +# Sources +[1] https://www.youtube.com/watch?v=j-3vuBynnOE +
+[2] https://www.youtube.com/watch?v=i2_zJ0ANrw0 +
+[3] https://keras.io/examples/vision/image_classification_with_vision_transformer/ +
+[4] https://towardsdatascience.com/data-normalization-in-machine-learning-395fdec69d02#:~:text=The%20short%20answer%20is%20%E2%80%94%20it,because%20they%20are%20bigger%20numbers. +
+[5] https://www.v7labs.com/blog/train-validation-test-set#:~:text=In%20general%2C%20putting%2080%25%20of,dimension%20of%20the%20data%2C%20etc. +
+[6] https://nanonets.com/blog/data-augmentation-how-to-use-deep-learning-when-you-have-limited-data-part-2/ +
+[7] https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0 + + +# Note for tutor +For my git logs I have had the problem of pushing my algorithm from the cluster. Therefore, I have instead for my solution created a local copy of it and created git logs that I backtracked based on my previous commits. As such most commits done are all done in the span of 2 days. + +I have provided screenshots as proof for this + + +
+ +
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