Commit1

1. Dataset_Maker.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from shutil import copyfile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make Directories "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Seperate 40x, 100x, 200x, 400x images into seperate directories. \n",
+    "# Each element of the below list is a path to the directory.\n",
+    "directories = ['tf_files/breast_40', 'tf_files/breast_100', 'tf_files/breast_200', 'tf_files/breast_400']\n",
+    "\n",
+    "for directory in directories:\n",
+    "    # Make directory if it does not exist\n",
+    "    if not os.path.exists(directory):\n",
+    "        os.makedirs(directory)\n",
+    "\n",
+    "    directory_b = directory+'/benign'\n",
+    "    directory_m = directory+'/malignant'\n",
+    "    # Make benign and malignant sub-directories for each directory if they do not exist\n",
+    "    if not os.path.exists(directory_b):\n",
+    "        os.makedirs(directory_b)\n",
+    "    if not os.path.exists(directory_m):\n",
+    "        os.makedirs(directory_m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Copy Benign Files\n",
+    "Change file paths to fit your setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "benign_types = os.listdir('/breast/benign/SOB')\n",
+    "# Store the types. Example: 'adenosis', 'fibroadenoma', 'phyllodes_tumor', 'tubular_adenoma'.\n",
+    "benign_types = [x for x in benign_types if x!= '.DS_Store']\n",
+    "\n",
+    "zooms = ['40', '100', '200', '400']\n",
+    "\n",
+    "# Copy files from benign to other folder\n",
+    "for benign_type in benign_types:\n",
+    "    patients = os.listdir('/breast/benign/SOB/'+benign_type)\n",
+    "    patients = [x for x in patients if x!= '.DS_Store']\n",
+    "    \n",
+    "    for patient in patients:\n",
+    "        for zoom in zooms:\n",
+    "            images = os.listdir('/breast/benign/SOB/'+benign_type+'/'+patient+'/'+zoom+'X')\n",
+    "            images = [x for x in images if x!= '.DS_Store']\n",
+    "            for image in images:\n",
+    "                copyfile('/breast/benign/SOB/'+benign_type+'/'+patient+'/'+zoom+'X'+'/'+image, 'tf_files/breast_'+zoom+'/benign/'+image)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Copy Malignant Files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "malignant_types = os.listdir('/breast/malignant/SOB')\n",
+    "# Store the types. Example: 'ductal_carcinoma', 'lobular_carcinoma', 'mucinous_carcinoma', 'papillary_carcinoma'.\n",
+    "malignant_types = [x for x in malignant_types if x!= '.DS_Store']\n",
+    "\n",
+    "zooms = ['40', '100', '200', '400']\n",
+    "\n",
+    "# Copy files from benign to other folder\n",
+    "for malignant_type in malignant_types:\n",
+    "    patients = os.listdir('/breast/malignant/SOB/'+malignant_type)\n",
+    "    patients = [x for x in patients if x!= '.DS_Store']\n",
+    "    \n",
+    "    for patient in patients:\n",
+    "        for zoom in zooms:\n",
+    "            images = os.listdir('/breast/malignant/SOB/'+malignant_type+'/'+patient+'/'+zoom+'X')\n",
+    "            images = [x for x in images if x!= '.DS_Store']\n",
+    "            for image in images:\n",
+    "                copyfile('/breast/malignant/SOB/'+malignant_type+'/'+patient+'/'+zoom+'X'+'/'+image, 'tf_files/breast_'+zoom+'/malignant/'+image)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

2. Transfer_Learning.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download Pre-Trained Version of Inception-v3 for Transfer Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! python classify.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Commands that can be used for retrain.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! python retrain.py -h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train 9 classifiers for each magnification\n",
+    "36 classifiers in total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zooms = [40, 100, 200, 400]\n",
+    "test_percentages = [10, 20, 30, 40, 50, 60, 70, 80, 85]\n",
+    "\n",
+    "for zoom in zooms:\n",
+    "    for test_percentage in test_percentages:\n",
+    "        ! python retrain.py --summaries_dir=tf_files/{zoom}_{test_percentage} --output_graph=tf_files/breast{zoom}_graph_{test_percentage}.pb --output_labels=tf_files/breast{zoom}_labels_{test_percentage}.txt --testing_percentage={test_percentage} --image_dir=tf_files/breast_{zoom}\n",
+    "    \n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check training progress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace tf_files/40_10 with the path to your summaries_sir\n",
+    "! tensorboard --logdir tf_files/40_10"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}