Datasets
Models
Downloads: 1
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{
"cells": [
{
"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 collections\n",
"import sys\n",
"import glob\n",
"import random\n",
"import cv2\n",
"import tensorflow as tf\n",
"import multiprocessing\n",
"\n",
"from math import ceil, floor\n",
"from copy import deepcopy\n",
"from tqdm import tqdm\n",
"from imgaug import augmenters as iaa\n",
"\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": [
{
"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: scikit-image in /opt/conda/lib/python3.6/site-packages (from efficientnet) (0.16.2)\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: pillow>=4.3.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (5.4.1)\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: scipy>=0.19.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (1.2.1)\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: imageio>=2.3.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (2.6.1)\n",
"Requirement already satisfied: PyWavelets>=0.4.0 in /opt/conda/lib/python3.6/site-packages (from scikit-image->efficientnet) (1.1.1)\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: 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: 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: 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: 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: 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: scikit-learn in /opt/conda/lib/python3.6/site-packages (from iterative-stratification) (0.21.3)\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 Modules from internet\n",
"!pip install efficientnet\n",
"!pip install iterative-stratification"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Import Custom Modules\n",
"import efficientnet.keras as efn \n",
"from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
},
"outputs": [],
"source": [
"# Seed\n",
"SEED = 12345\n",
"np.random.seed(SEED)\n",
"# tf.set_random_seed(SEED)\n",
"\n",
"# Constants\n",
"TEST_SIZE = 0.01\n",
"HEIGHT = 256\n",
"WIDTH = 256\n",
"CHANNELS = 3\n",
"TRAIN_BATCH_SIZE = 32\n",
"VALID_BATCH_SIZE = 64\n",
"SHAPE = (HEIGHT, WIDTH, CHANNELS)\n",
"\n",
"# Folders\n",
"DATA_DIR = '/kaggle/input/rsna-intracranial-hemorrhage-detection/rsna-intracranial-hemorrhage-detection/'\n",
"TEST_IMAGES_DIR = DATA_DIR + 'stage_2_test/'\n",
"TRAIN_IMAGES_DIR = DATA_DIR + 'stage_2_train/'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stage_2_sample_submission.csv stage_2_test stage_2_train stage_2_train.csv\r\n"
]
}
],
"source": [
"!ls '/kaggle/input/rsna-intracranial-hemorrhage-detection/rsna-intracranial-hemorrhage-detection'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def correct_dcm(dcm):\n",
" x = dcm.pixel_array + 1000\n",
" px_mode = 4096\n",
" x[x>=px_mode] = x[x>=px_mode] - px_mode\n",
" dcm.PixelData = x.tobytes()\n",
" dcm.RescaleIntercept = -1000\n",
"\n",
"def window_image(dcm, window_center, window_width): \n",
" if (dcm.BitsStored == 12) and (dcm.PixelRepresentation == 0) and (int(dcm.RescaleIntercept) > -100):\n",
" correct_dcm(dcm)\n",
" img = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept\n",
" \n",
" # Resize\n",
" img = cv2.resize(img, SHAPE[:2], interpolation = cv2.INTER_LINEAR)\n",
" \n",
" img_min = window_center - window_width // 2\n",
" img_max = window_center + window_width // 2\n",
" img = np.clip(img, img_min, img_max)\n",
" return img\n",
"\n",
"def bsb_window(dcm):\n",
" brain_img = window_image(dcm, 40, 80)\n",
" subdural_img = window_image(dcm, 80, 200)\n",
" soft_img = window_image(dcm, 40, 380)\n",
" \n",
" brain_img = (brain_img - 0) / 80\n",
" subdural_img = (subdural_img - (-20)) / 200\n",
" soft_img = (soft_img - (-150)) / 380\n",
" bsb_img = np.array([brain_img, subdural_img, soft_img]).transpose(1,2,0)\n",
" return bsb_img\n",
"\n",
"def _read(path, SHAPE):\n",
" dcm = pydicom.dcmread(path)\n",
" try:\n",
" img = bsb_window(dcm)\n",
" except:\n",
" img = np.zeros(SHAPE)\n",
" return img"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import the training and test datasets."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"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 [==============================] - 0s 0us/step\n"
]
}
],
"source": [
"base_model = efn.EfficientNetB0(weights = 'imagenet', include_top = False, \\\n",
" pooling = 'avg', input_shape = (HEIGHT, WIDTH, 3))\n",
"\n",
"x = base_model.output\n",
"x = Dropout(0.125)(x)\n",
"output_layer = Dense(6, activation = 'sigmoid')(x)\n",
"model = Model(inputs=base_model.input, outputs=output_layer)\n",
"model.compile(optimizer = Adam(learning_rate = 0.0001), \n",
" loss = 'binary_crossentropy',\n",
" metrics = ['acc'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting gdown\n",
" Downloading https://files.pythonhosted.org/packages/b0/b4/a8e9d0b02bca6aa53087001abf064cc9992bda11bd6840875b8098d93573/gdown-3.8.3.tar.gz\n",
"Requirement already satisfied: filelock in /opt/conda/lib/python3.6/site-packages (from gdown) (3.0.12)\n",
"Requirement already satisfied: requests in /opt/conda/lib/python3.6/site-packages (from gdown) (2.22.0)\n",
"Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from gdown) (1.12.0)\n",
"Requirement already satisfied: tqdm in /opt/conda/lib/python3.6/site-packages (from gdown) (4.36.1)\n",
"Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.6/site-packages (from requests->gdown) (3.0.4)\n",
"Requirement already satisfied: idna<2.9,>=2.5 in /opt/conda/lib/python3.6/site-packages (from requests->gdown) (2.8)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.6/site-packages (from requests->gdown) (2019.9.11)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.6/site-packages (from requests->gdown) (1.24.2)\n",
"Building wheels for collected packages: gdown\n",
" Building wheel for gdown (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for gdown: filename=gdown-3.8.3-cp36-none-any.whl size=8850 sha256=60eafdb73efc62d7de20834e8aece4a416db33f8fa7b5c0536f594bc52163a15\n",
" Stored in directory: /tmp/.cache/pip/wheels/a7/9d/16/9e0bda9a327ff2cddaee8de48a27553fb1efce73133593d066\n",
"Successfully built gdown\n",
"Installing collected packages: gdown\n",
"Successfully installed gdown-3.8.3\n"
]
}
],
"source": [
"!pip install gdown"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1FXF1HymYbRf3OlThMTXAa74TRup3AhD_\n",
"To: /kaggle/working/model_effnet_bo_087.h5\n",
"16.7MB [00:00, 151MB/s]\n"
]
}
],
"source": [
"!gdown https://drive.google.com/uc?id=1FXF1HymYbRf3OlThMTXAa74TRup3AhD_"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"model.load_weights('model_effnet_bo_087.h5')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(256, 256, 3)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img_data = _read(TRAIN_IMAGES_DIR + '/ID_12cadc6af.dcm', (256, 256))\n",
"img_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 256, 256, 3)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = np.empty((1, 256,256, 3))\n",
"X[0] = img_data\n",
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.02540314, 0.00152788, 0.0010443 , 0.0023855 , 0.00326541,\n",
" 0.00998502]], dtype=float32)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds = model.predict(X)\n",
"preds"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 6)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds.shape"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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