{

 "cells": [

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "# RSNA Intracranial Hemorrhage Detection "

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<b>Competition Overview</b><br/><br/>\n",

    "Intracranial hemorrhage, bleeding that occurs inside the cranium, is a serious health problem requiring rapid and often intensive medical treatment. For example, intracranial hemorrhages account for approximately 10% of strokes in the U.S., where stroke is the fifth-leading cause of death. Identifying the location and type of any hemorrhage present is a critical step in treating the patient.\n",

    "\n",

    "Diagnosis requires an urgent procedure. When a patient shows acute neurological symptoms such as severe headache or loss of consciousness, highly trained specialists review medical images of the patient’s cranium to look for the presence, location and type of hemorrhage. The process is complicated and often time consuming."

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<b>What am i predicting?</b><br/><br/>\n",

    "In this competition our goal is to predict intracranial hemorrhage and its subtypes. Given an image the we need to predict probablity of each subtype. This indicates its a multilabel classification problem."

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<b>Competition Evaluation Metric</b><br/><br/>\n",

    "Evaluation metric is weighted multi-label logarithmic loss. So for given image we need to predict probality for each subtype. There is also an any label, which indicates that a hemorrhage of ANY kind exists in the image. The any label is weighted more highly than specific hemorrhage sub-types.\n",

    "\n",

    "<b>Note:</b>The weights for each subtype for calculating weighted multi-label logarithmic loss is **not** given as part of the competition. We will be using binary cross entropy loss as weights are not available"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "<b>Dataset Description</b>\n",

    "\n",

    "The dataset is divided into two parts\n",

    "\n",

    "1. Train\n",

    "2. Test\n",

    "\n",

    "**1. Train**\n",

    "Number of rows: 40,45,548 records.\n",

    "Number of columns: 2\n",

    "\n",

    "Columns:\n",

    "\n",

    "**Id**: An image Id. Each Id corresponds to a unique image, and will contain an underscore.\n",

    "\n",

    "Example: ID_28fbab7eb_epidural. So the Id consists of two parts one is image file id ID_28fbab7eb and the other is sub type name\n",

    "\n",

    "**Label**: The target label whether that sub-type of hemorrhage (or any hemorrhage in the case of any) exists in the indicated image. 1 --> Exists and 0 --> Doesn't exist.\n",

    "\n",

    "**2. Test**\n",

    "Number of rows: 4,71,270 records.\n",

    "\n",

    "Columns:\n",

    "\n",

    "**Id**: An image Id. Each Id corresponds to a unique image, and will contain an underscore.\n",

    "\n",

    "Example: ID_28fbab7eb_epidural. So the Id consists of two parts one is image file id ID_28fbab7eb and the other is sub type name"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [

    {

     "name": "stderr",

     "output_type": "stream",

     "text": [

      "Using TensorFlow backend.\n"

     ]

    }

   ],

   "source": [

    "import numpy as np\n",

    "import pandas as pd\n",

    "import pydicom\n",

    "import os\n",

    "import glob\n",

    "import random\n",

    "import cv2\n",

    "import tensorflow as tf\n",

    "from math import ceil, floor\n",

    "from tqdm import tqdm\n",

    "from imgaug import augmenters as iaa\n",

    "import matplotlib.pyplot as plt\n",

    "from math import ceil, floor\n",

    "import keras\n",

    "import keras.backend as K\n",

    "from keras.callbacks import Callback, ModelCheckpoint\n",

    "from keras.layers import Dense, Flatten, Dropout\n",

    "from keras.models import Model, load_model\n",

    "from keras.utils import Sequence\n",

    "from keras.losses import binary_crossentropy\n",

    "from keras.optimizers import Adam"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},

   "outputs": [],

   "source": [

    "# Random Seed\n",

    "SEED = 42\n",

    "np.random.seed(SEED)\n",

    "\n",

    "# some constants\n",

    "TEST_SIZE = 0.06\n",

    "HEIGHT = 256\n",

    "WIDTH = 256\n",

    "TRAIN_BATCH_SIZE = 32\n",

    "VALID_BATCH_SIZE = 64\n",

    "\n",

    "# Train and Test folders\n",

    "input_folder = '../input/rsna-intracranial-hemorrhage-detection/'\n",

    "path_train_img = input_folder + 'stage_1_train_images/'\n",

    "path_test_img = input_folder + 'stage_1_test_images/'"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

       "<style scoped>\n",

       "    .dataframe tbody tr th:only-of-type {\n",

       "        vertical-align: middle;\n",

       "    }\n",

       "\n",

       "    .dataframe tbody tr th {\n",

       "        vertical-align: top;\n",

       "    }\n",

       "\n",

       "    .dataframe thead th {\n",

       "        text-align: right;\n",

       "    }\n",

       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>ID</th>\n",

       "      <th>Label</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>ID_63eb1e259_epidural</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>ID_63eb1e259_intraparenchymal</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>ID_63eb1e259_intraventricular</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>ID_63eb1e259_subarachnoid</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>ID_63eb1e259_subdural</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                              ID  Label\n",

       "0          ID_63eb1e259_epidural      0\n",

       "1  ID_63eb1e259_intraparenchymal      0\n",

       "2  ID_63eb1e259_intraventricular      0\n",

       "3      ID_63eb1e259_subarachnoid      0\n",

       "4          ID_63eb1e259_subdural      0"

      ]

     },

     "execution_count": 3,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "train_df = pd.read_csv(input_folder + 'stage_1_train.csv')\n",

    "train_df.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

       "<style scoped>\n",

       "    .dataframe tbody tr th:only-of-type {\n",

       "        vertical-align: middle;\n",

       "    }\n",

       "\n",

       "    .dataframe tbody tr th {\n",

       "        vertical-align: top;\n",

       "    }\n",

       "\n",

       "    .dataframe thead th {\n",

       "        text-align: right;\n",

       "    }\n",

       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>ID</th>\n",

       "      <th>Label</th>\n",

       "      <th>sub_type</th>\n",

       "      <th>file_name</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>ID_63eb1e259_epidural</td>\n",

       "      <td>0</td>\n",

       "      <td>epidural</td>\n",

       "      <td>ID_63eb1e259.dcm</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>ID_63eb1e259_intraparenchymal</td>\n",

       "      <td>0</td>\n",

       "      <td>intraparenchymal</td>\n",

       "      <td>ID_63eb1e259.dcm</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>ID_63eb1e259_intraventricular</td>\n",

       "      <td>0</td>\n",

       "      <td>intraventricular</td>\n",

       "      <td>ID_63eb1e259.dcm</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>ID_63eb1e259_subarachnoid</td>\n",

       "      <td>0</td>\n",

       "      <td>subarachnoid</td>\n",

       "      <td>ID_63eb1e259.dcm</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>ID_63eb1e259_subdural</td>\n",

       "      <td>0</td>\n",

       "      <td>subdural</td>\n",

       "      <td>ID_63eb1e259.dcm</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                              ID  Label          sub_type         file_name\n",

       "0          ID_63eb1e259_epidural      0          epidural  ID_63eb1e259.dcm\n",

       "1  ID_63eb1e259_intraparenchymal      0  intraparenchymal  ID_63eb1e259.dcm\n",

       "2  ID_63eb1e259_intraventricular      0  intraventricular  ID_63eb1e259.dcm\n",

       "3      ID_63eb1e259_subarachnoid      0      subarachnoid  ID_63eb1e259.dcm\n",

       "4          ID_63eb1e259_subdural      0          subdural  ID_63eb1e259.dcm"

      ]

     },

     "execution_count": 4,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "# extract subtype\n",

    "train_df['sub_type'] = train_df['ID'].apply(lambda x: x.split('_')[-1])\n",

    "# extract filename\n",

    "train_df['file_name'] = train_df['ID'].apply(lambda x: '_'.join(x.split('_')[:2]) + '.dcm')\n",

    "train_df.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(4045572, 4)"

      ]

     },

     "execution_count": 5,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "train_df.shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 6,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(4045548, 4)"

      ]

     },

     "execution_count": 6,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "# remove duplicates\n",

    "train_df.drop_duplicates(['Label', 'sub_type', 'file_name'], inplace=True)\n",

    "train_df.shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Number of train images availabe: 674258\n"

     ]

    }

   ],

   "source": [

    "print(\"Number of train images availabe:\", len(os.listdir(path_train_img)))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 8,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

       "<style scoped>\n",

       "    .dataframe tbody tr th:only-of-type {\n",

       "        vertical-align: middle;\n",

       "    }\n",

       "\n",

       "    .dataframe tbody tr th {\n",

       "        vertical-align: top;\n",

       "    }\n",

       "\n",

       "    .dataframe thead th {\n",

       "        text-align: right;\n",

       "    }\n",

       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th>sub_type</th>\n",

       "      <th>any</th>\n",

       "      <th>epidural</th>\n",

       "      <th>intraparenchymal</th>\n",

       "      <th>intraventricular</th>\n",

       "      <th>subarachnoid</th>\n",

       "      <th>subdural</th>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>file_name</th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>ID_000039fa0.dcm</th>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>ID_00005679d.dcm</th>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>ID_00008ce3c.dcm</th>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>ID_0000950d7.dcm</th>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>ID_0000aee4b.dcm</th>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "      <td>0</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "sub_type          any  epidural  intraparenchymal  intraventricular  \\\n",

       "file_name                                                             \n",

       "ID_000039fa0.dcm    0         0                 0                 0   \n",

       "ID_00005679d.dcm    0         0                 0                 0   \n",

       "ID_00008ce3c.dcm    0         0                 0                 0   \n",

       "ID_0000950d7.dcm    0         0                 0                 0   \n",

       "ID_0000aee4b.dcm    0         0                 0                 0   \n",

       "\n",

       "sub_type          subarachnoid  subdural  \n",

       "file_name                                 \n",

       "ID_000039fa0.dcm             0         0  \n",

       "ID_00005679d.dcm             0         0  \n",

       "ID_00008ce3c.dcm             0         0  \n",

       "ID_0000950d7.dcm             0         0  \n",

       "ID_0000aee4b.dcm             0         0  "

      ]

     },

     "execution_count": 8,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "train_final_df = pd.pivot_table(train_df.drop(columns='ID'), index=\"file_name\", \\\n",

    "                                columns=\"sub_type\", values=\"Label\")\n",

    "train_final_df.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 9,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(674258, 6)"

      ]

     },

     "execution_count": 9,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "train_final_df.shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 10,

   "metadata": {},

   "outputs": [],

   "source": [

    "# Invalid image ID_6431af929.dcm\n",

    "train_final_df.drop('ID_6431af929.dcm', inplace=True)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 11,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Collecting efficientnet\n",

      "  Downloading https://files.pythonhosted.org/packages/97/82/f3ae07316f0461417dc54affab6e86ab188a5a22f33176d35271628b96e0/efficientnet-1.0.0-py3-none-any.whl\n",

      "Requirement already satisfied: keras-applications<=1.0.8,>=1.0.7 in /opt/conda/lib/python3.6/site-packages (from efficientnet) (1.0.8)\n",

      "Requirement already satisfied: scikit-image in /opt/conda/lib/python3.6/site-packages (from efficientnet) (0.16.1)\n",

      "Requirement already satisfied: numpy>=1.9.1 in /opt/conda/lib/python3.6/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.16.4)\n",

      "Requirement already satisfied: h5py in /opt/conda/lib/python3.6/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (2.9.0)\n",

      "Requirement already satisfied: PyWavelets>=0.4.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (1.0.3)\n",

      "Requirement already satisfied: scipy>=0.19.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (1.2.1)\n",

      "Requirement already satisfied: networkx>=2.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (2.4)\n",

      "Requirement already satisfied: imageio>=2.3.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (2.6.0)\n",

      "Requirement already satisfied: pillow>=4.3.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (5.4.1)\n",

      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (3.0.3)\n",

      "Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from h5py->keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.12.0)\n",

      "Requirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.6/site-packages (from networkx>=2.0->scikit-image->efficientnet) (4.4.0)\n",

      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (0.10.0)\n",

      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (1.1.0)\n",

      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.4.2)\n",

      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.8.0)\n",

      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (41.4.0)\n",

      "Installing collected packages: efficientnet\n",

      "Successfully installed efficientnet-1.0.0\n",

      "Collecting iterative-stratification\n",

      "  Downloading https://files.pythonhosted.org/packages/9d/79/9ba64c8c07b07b8b45d80725b2ebd7b7884701c1da34f70d4749f7b45f9a/iterative_stratification-0.1.6-py3-none-any.whl\n",

      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.6/site-packages (from iterative-stratification) (0.21.3)\n",

      "Requirement already satisfied: scipy in /opt/conda/lib/python3.6/site-packages (from iterative-stratification) (1.2.1)\n",

      "Requirement already satisfied: numpy in /opt/conda/lib/python3.6/site-packages (from iterative-stratification) (1.16.4)\n",

      "Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.6/site-packages (from scikit-learn->iterative-stratification) (0.13.2)\n",

      "Installing collected packages: iterative-stratification\n",

      "Successfully installed iterative-stratification-0.1.6\n"

     ]

    }

   ],

   "source": [

    "# Install Efficient Net as it is not part of Keras\n",

    "!pip install efficientnet\n",

    "!pip install iterative-stratification"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 12,

   "metadata": {},

   "outputs": [],

   "source": [

    "import efficientnet.keras as efn \n",

    "from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 13,

   "metadata": {},

   "outputs": [],

   "source": [

    "from IPython.display import HTML\n",

    "\n",

    "def create_download_link(title = \"Download CSV file\", filename = \"data.csv\"):  \n",

    "    \"\"\"\n",

    "    Helper function to generate download link to files in kaggle kernel \n",

    "    \"\"\"\n",

    "    html = '<a href={filename}>{title}</a>'\n",

    "    html = html.format(title=title,filename=filename)\n",

    "    return HTML(html)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 14,

   "metadata": {},

   "outputs": [],

   "source": [

    "def get_dicom_field_value(val):\n",

    "    \"\"\"\n",

    "    Helper function to get value of dicom field in dicom file\n",

    "    \"\"\"\n",

    "    if type(val) == pydicom.multival.MultiValue:\n",

    "        return int(val[0])\n",

    "    else:\n",

    "        return int(val)\n",

    "\n",

    "def get_windowing(data):\n",

    "    \"\"\"\n",

    "    Helper function to extract meta data features in dicom file\n",

    "    return: window center, window width, slope, intercept\n",

    "    \"\"\"\n",

    "    dicom_fields = [data.WindowCenter, data.WindowWidth, data.RescaleSlope, data.RescaleIntercept]\n",

    "    return [get_dicom_field_value(x) for x in dicom_fields]\n",

    "\n",

    "\n",

    "def get_windowed_image(image, wc, ww, slope, intercept):\n",

    "    \"\"\"\n",

    "    Helper function to construct windowed image from meta data features\n",

    "    return: windowed image\n",

    "    \"\"\"\n",

    "    img = (image*slope +intercept)\n",

    "    img_min = wc - ww//2\n",

    "    img_max = wc + ww//2\n",

    "    img[img<img_min] = img_min\n",

    "    img[img>img_max] = img_max\n",

    "    return img \n",

    "\n",

    "\n",

    "def _normalize(img):\n",

    "    if img.max() == img.min():\n",

    "        return np.zeros(img.shape)\n",

    "    return 2 * (img - img.min())/(img.max() - img.min()) - 1\n",

    "\n",

    "def _read(path, desired_size=(224, 224)):\n",

    "    \"\"\"\n",

    "    Helper function to generate windowed image \n",

    "    \"\"\"\n",

    "    # 1. read dicom file\n",

    "    dcm = pydicom.dcmread(path)\n",

    "    \n",

    "    # 2. Extract meta data features\n",

    "    # window center, window width, slope, intercept\n",

    "    window_params = get_windowing(dcm)\n",

    "\n",

    "    try:\n",

    "        # 3. Generate windowed image\n",

    "        img = get_windowed_image(dcm.pixel_array, *window_params)\n",

    "    except:\n",

    "        img = np.zeros(desired_size)\n",

    "\n",

    "    img = _normalize(img)\n",

    "\n",

    "    if desired_size != (512, 512):\n",

    "        # resize image\n",

    "        img = cv2.resize(img, desired_size, interpolation = cv2.INTER_LINEAR)\n",

    "    return img[:,:,np.newaxis]"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 15,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(128, 128, 1)"

      ]

     },

     "execution_count": 15,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "_read(path_train_img + 'ID_ffff922b9.dcm', (128, 128)).shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 16,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "<matplotlib.image.AxesImage at 0x7f821e7b95c0>"

      ]

     },

     "execution_count": 16,

     "metadata": {},

     "output_type": "execute_result"

    },

    {

     "data": {

      "image/png": 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\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    }

   ],

   "source": [

    "plt.imshow(\n",

    "    _read(path_train_img + 'ID_ffff922b9.dcm', (128, 128))[:, :, 0]\n",

    ")"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 17,

   "metadata": {},

   "outputs": [],

   "source": [

    "# Augmentations\n",

    "# Flip Left Right\n",

    "# Cropping\n",

    "sometimes = lambda aug: iaa.Sometimes(0.25, aug)\n",

    "augmentation = iaa.Sequential([  \n",

    "                                iaa.Fliplr(0.25),\n",

    "                                sometimes(iaa.Crop(px=(0, 25), keep_size = True, \n",

    "                                                   sample_independently = False))   \n",

    "                            ], random_order = True)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 18,

   "metadata": {},

   "outputs": [],

   "source": [

    "# Train Data Generator\n",

    "class TrainDataGenerator(keras.utils.Sequence):\n",

    "\n",

    "    def __init__(self, dataset, labels, batch_size=16, img_size=(512, 512), img_dir = path_train_img, \\\n",

    "                 augment = False, *args, **kwargs):\n",

    "        self.dataset = dataset\n",

    "        self.ids = dataset.index\n",

    "        self.labels = labels\n",

    "        self.batch_size = batch_size\n",

    "        self.img_size = img_size\n",

    "        self.img_dir = img_dir\n",

    "        self.augment = augment\n",

    "        self.on_epoch_end()\n",

    "\n",

    "    def __len__(self):\n",

    "        return int(ceil(len(self.ids) / self.batch_size))\n",

    "\n",

    "    def __getitem__(self, index):\n",

    "        indices = self.indices[index*self.batch_size:(index+1)*self.batch_size]\n",

    "        X, Y = self.__data_generation(indices)\n",

    "        return X, Y\n",

    "\n",

    "    def augmentor(self, image):\n",

    "        augment_img = augmentation        \n",

    "        image_aug = augment_img.augment_image(image)\n",

    "        return image_aug\n",

    "\n",

    "    def on_epoch_end(self):\n",

    "        self.indices = np.arange(len(self.ids))\n",

    "        np.random.shuffle(self.indices)\n",

    "        \n",

    "    def __data_generation(self, indices):\n",

    "        X = np.empty((self.batch_size, *self.img_size, 3))\n",

    "        Y = np.empty((self.batch_size, 6), dtype=np.float32)\n",

    "        \n",

    "        for i, index in enumerate(indices):\n",

    "            ID = self.ids[index]\n",

    "            image = _read(self.img_dir + ID, self.img_size)\n",

    "            if self.augment:\n",

    "                X[i,] = self.augmentor(image)\n",

    "            else:\n",

    "                X[i,] = image            \n",

    "            Y[i,] = self.labels.iloc[index].values        \n",

    "        return X, Y\n",

    "    \n",

    "class TestDataGenerator(keras.utils.Sequence):\n",

    "    def __init__(self, ids, labels, batch_size = 5, img_size = (512, 512), img_dir = path_test_img, \\\n",

    "                 *args, **kwargs):\n",

    "        self.ids = ids\n",

    "        self.labels = labels\n",

    "        self.batch_size = batch_size\n",

    "        self.img_size = img_size\n",

    "        self.img_dir = img_dir\n",

    "        self.on_epoch_end()\n",

    "\n",

    "    def __len__(self):\n",

    "        return int(ceil(len(self.ids) / self.batch_size))\n",

    "\n",

    "    def __getitem__(self, index):\n",

    "        indices = self.indices[index*self.batch_size:(index+1)*self.batch_size]\n",

    "        list_IDs_temp = [self.ids[k] for k in indices]\n",

    "        X = self.__data_generation(list_IDs_temp)\n",

    "        return X\n",

    "\n",

    "    def on_epoch_end(self):\n",

    "        self.indices = np.arange(len(self.ids))\n",

    "\n",

    "    def __data_generation(self, list_IDs_temp):\n",

    "        X = np.empty((self.batch_size, *self.img_size, 3))\n",

    "        for i, ID in enumerate(list_IDs_temp):\n",

    "            image = _read(self.img_dir + ID, self.img_size)\n",

    "            X[i,] = image            \n",

    "        return X"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "As we have seen in EDA notebook that we have very few epidural subtypes so we need oversample this sub type"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 19,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Train Shape: (677018, 6)\n"

     ]

    }

   ],

   "source": [

    "# Oversampling\n",

    "epidural_df = train_final_df[train_final_df.epidural == 1]\n",

    "train_final_df = pd.concat([train_final_df, epidural_df])\n",

    "print('Train Shape: {}'.format(train_final_df.shape))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 20,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

       "<style scoped>\n",

       "    .dataframe tbody tr th:only-of-type {\n",

       "        vertical-align: middle;\n",

       "    }\n",

       "\n",

       "    .dataframe tbody tr th {\n",

       "        vertical-align: top;\n",

       "    }\n",

       "\n",

       "    .dataframe thead th {\n",

       "        text-align: right;\n",

       "    }\n",

       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>ID</th>\n",

       "      <th>Label</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>ID_28fbab7eb_epidural</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>ID_28fbab7eb_intraparenchymal</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>ID_28fbab7eb_intraventricular</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>ID_28fbab7eb_subarachnoid</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>ID_28fbab7eb_subdural</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "                              ID  Label\n",

       "0          ID_28fbab7eb_epidural    0.5\n",

       "1  ID_28fbab7eb_intraparenchymal    0.5\n",

       "2  ID_28fbab7eb_intraventricular    0.5\n",

       "3      ID_28fbab7eb_subarachnoid    0.5\n",

       "4          ID_28fbab7eb_subdural    0.5"

      ]

     },

     "execution_count": 20,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "# load test set\n",

    "test_df = pd.read_csv(input_folder + 'stage_1_sample_submission.csv')\n",

    "test_df.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 21,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "(78545, 6)"

      ]

     },

     "execution_count": 21,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "# extract subtype\n",

    "test_df['sub_type'] = test_df['ID'].apply(lambda x: x.split('_')[-1])\n",

    "# extract filename\n",

    "test_df['file_name'] = test_df['ID'].apply(lambda x: '_'.join(x.split('_')[:2]) + '.dcm')\n",

    "\n",

    "test_df = pd.pivot_table(test_df.drop(columns='ID'), index=\"file_name\", \\\n",

    "                                columns=\"sub_type\", values=\"Label\")\n",

    "test_df.head()\n",

    "\n",

    "test_df.shape"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 22,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

       "<style scoped>\n",

       "    .dataframe tbody tr th:only-of-type {\n",

       "        vertical-align: middle;\n",

       "    }\n",

       "\n",

       "    .dataframe tbody tr th {\n",

       "        vertical-align: top;\n",

       "    }\n",

       "\n",

       "    .dataframe thead th {\n",

       "        text-align: right;\n",

       "    }\n",

       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th>sub_type</th>\n",

       "      <th>any</th>\n",

       "      <th>epidural</th>\n",

       "      <th>intraparenchymal</th>\n",

       "      <th>intraventricular</th>\n",

       "      <th>subarachnoid</th>\n",

       "      <th>subdural</th>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>file_name</th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "      <th></th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>ID_000012eaf.dcm</th>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>ID_0000ca2f6.dcm</th>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>ID_000259ccf.dcm</th>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>ID_0002d438a.dcm</th>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>ID_00032d440.dcm</th>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "      <td>0.5</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "sub_type          any  epidural  intraparenchymal  intraventricular  \\\n",

       "file_name                                                             \n",

       "ID_000012eaf.dcm  0.5       0.5               0.5               0.5   \n",

       "ID_0000ca2f6.dcm  0.5       0.5               0.5               0.5   \n",

       "ID_000259ccf.dcm  0.5       0.5               0.5               0.5   \n",

       "ID_0002d438a.dcm  0.5       0.5               0.5               0.5   \n",

       "ID_00032d440.dcm  0.5       0.5               0.5               0.5   \n",

       "\n",

       "sub_type          subarachnoid  subdural  \n",

       "file_name                                 \n",

       "ID_000012eaf.dcm           0.5       0.5  \n",

       "ID_0000ca2f6.dcm           0.5       0.5  \n",

       "ID_000259ccf.dcm           0.5       0.5  \n",

       "ID_0002d438a.dcm           0.5       0.5  \n",

       "ID_00032d440.dcm           0.5       0.5  "

      ]

     },

     "execution_count": 22,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "test_df.head()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 23,

   "metadata": {

    "scrolled": false

   },

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "Downloading data from https://github.com/Callidior/keras-applications/releases/download/efficientnet/efficientnet-b0_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5\n",

      "16809984/16804768 [==============================] - 1s 0us/step\n",

      "Model: \"model_1\"\n",

      "__________________________________________________________________________________________________\n",

      "Layer (type)                    Output Shape         Param #     Connected to                     \n",

      "==================================================================================================\n",

      "input_1 (InputLayer)            (None, 256, 256, 3)  0                                            \n",

      "__________________________________________________________________________________________________\n",

      "stem_conv (Conv2D)              (None, 128, 128, 32) 864         input_1[0][0]                    \n",

      "__________________________________________________________________________________________________\n",

      "stem_bn (BatchNormalization)    (None, 128, 128, 32) 128         stem_conv[0][0]                  \n",

      "__________________________________________________________________________________________________\n",

      "stem_activation (Activation)    (None, 128, 128, 32) 0           stem_bn[0][0]                    \n",

      "__________________________________________________________________________________________________\n",

      "block1a_dwconv (DepthwiseConv2D (None, 128, 128, 32) 288         stem_activation[0][0]            \n",

      "__________________________________________________________________________________________________\n",

      "block1a_bn (BatchNormalization) (None, 128, 128, 32) 128         block1a_dwconv[0][0]             \n",

      "__________________________________________________________________________________________________\n",

      "block1a_activation (Activation) (None, 128, 128, 32) 0           block1a_bn[0][0]                 \n",

      "__________________________________________________________________________________________________\n",

      "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",

      "__________________________________________________________________________________________________\n",

      "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",

      "__________________________________________________________________________________________________\n",

      "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",

      "__________________________________________________________________________________________________\n",

      "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",

      "__________________________________________________________________________________________________\n",

      "block1a_se_excite (Multiply)    (None, 128, 128, 32) 0           block1a_activation[0][0]         \n",

      "                                                                 block1a_se_expand[0][0]          \n",

      "__________________________________________________________________________________________________\n",

      "block1a_project_conv (Conv2D)   (None, 128, 128, 16) 512         block1a_se_excite[0][0]          \n",

      "__________________________________________________________________________________________________\n",

      "block1a_project_bn (BatchNormal (None, 128, 128, 16) 64          block1a_project_conv[0][0]       \n",

      "__________________________________________________________________________________________________\n",

      "block2a_expand_conv (Conv2D)    (None, 128, 128, 96) 1536        block1a_project_bn[0][0]         \n",

      "__________________________________________________________________________________________________\n",

      "block2a_expand_bn (BatchNormali (None, 128, 128, 96) 384         block2a_expand_conv[0][0]        \n",

      "__________________________________________________________________________________________________\n",

      "block2a_expand_activation (Acti (None, 128, 128, 96) 0           block2a_expand_bn[0][0]          \n",

      "__________________________________________________________________________________________________\n",

      "block2a_dwconv (DepthwiseConv2D (None, 64, 64, 96)   864         block2a_expand_activation[0][0]  \n",

      "__________________________________________________________________________________________________\n",

      "block2a_bn (BatchNormalization) (None, 64, 64, 96)   384         block2a_dwconv[0][0]             \n",

      "__________________________________________________________________________________________________\n",

      "block2a_activation (Activation) (None, 64, 64, 96)   0           block2a_bn[0][0]                 \n",

      "__________________________________________________________________________________________________\n",

      "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",

      "__________________________________________________________________________________________________\n",

      "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",