{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"'''\nOnline\n'''\n!pip install -qU python-gdcm pydicom pylibjpeg\n\n'''\nOffline (need to add dataset --> for-pydicom)\n'''\n\n#!pip install -qU /kaggle/input/for-pydicom/python_gdcm-3.0.14-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl /kaggle/input/for-pydicom/pylibjpeg-1.4.0-py3-none-any.whl --find-links frozen_packages --no-index","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:58:53.493221Z","iopub.execute_input":"2023-01-13T15:58:53.494008Z","iopub.status.idle":"2023-01-13T15:59:15.592347Z","shell.execute_reply.started":"2023-01-13T15:58:53.493919Z","shell.execute_reply":"2023-01-13T15:59:15.591396Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport cv2\nimport os\nimport time\n\nimport tensorflow as tf\n\nfrom tensorflow import keras\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization, Activation\n\nfrom tensorflow.keras.layers.experimental.preprocessing import RandomFlip, RandomZoom, RandomRotation, RandomTranslation\n\nimport matplotlib.pyplot as plt\nimport pydicom\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\nimport gdcm\nimport pylibjpeg","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-01-13T15:59:15.594556Z","iopub.execute_input":"2023-01-13T15:59:15.595147Z","iopub.status.idle":"2023-01-13T15:59:19.710103Z","shell.execute_reply.started":"2023-01-13T15:59:15.595102Z","shell.execute_reply":"2023-01-13T15:59:19.709303Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"c_0 = np.array([2, 48, 71,256])/256\nc_1 = np.array([251, 133, 0,256])/256","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.711174Z","iopub.execute_input":"2023-01-13T15:59:19.711458Z","iopub.status.idle":"2023-01-13T15:59:19.718107Z","shell.execute_reply.started":"2023-01-13T15:59:19.711423Z","shell.execute_reply":"2023-01-13T15:59:19.717247Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Because of incompatibilities with the newest version, Tensorflow has been downgraded. In order to keep compatibility with the GPU drivers, a compatible notebook has directly been used. The notebook corresponds to the project of Arun Nadaradjane ([see](https://www.kaggle.com/code/aaron1288/very-simple-deep-learning-strategy))","metadata":{}},{"cell_type":"code","source":"print(tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.720578Z","iopub.execute_input":"2023-01-13T15:59:19.721081Z","iopub.status.idle":"2023-01-13T15:59:19.727766Z","shell.execute_reply.started":"2023-01-13T15:59:19.721046Z","shell.execute_reply":"2023-01-13T15:59:19.727028Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1. Data preprocessing\n\nAs observed in the EDA, the metadata as well as the scans could be cleaned. The metadata are cleaned first. This way, only the scans of the selected patients are processed in a second time.","metadata":{}},{"cell_type":"markdown","source":"## 1.1. Metadata preprocessing","metadata":{}},{"cell_type":"markdown","source":"Taking into acount the observation in the EDA, the dataframe containing the metadata is cleaned.","metadata":{}},{"cell_type":"code","source":"df_train_raw = pd.read_csv('/kaggle/input/rsna-breast-cancer-detection/train.csv')\ndf_test_raw = pd.read_csv('/kaggle/input/rsna-breast-cancer-detection/test.csv')\n\ntrain_path = '/kaggle/input/rsna-breast-cancer-detection/train_images/'\ntest_path = '/kaggle/input/rsna-breast-cancer-detection/test_images/'\n\ndf_train_raw.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.729058Z","iopub.execute_input":"2023-01-13T15:59:19.729927Z","iopub.status.idle":"2023-01-13T15:59:19.853931Z","shell.execute_reply.started":"2023-01-13T15:59:19.729781Z","shell.execute_reply":"2023-01-13T15:59:19.853212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Keeping only CC and MLO","metadata":{}},{"cell_type":"markdown","source":"It has been observed in the EDA that the most present views where the CC and the MLO. Thus, only these views will be used for training the neural network.","metadata":{}},{"cell_type":"code","source":"df_train = df_train_raw.loc[(df_train_raw['view'] == 'MLO')|(df_train_raw['view'] == 'CC'), :]","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.855081Z","iopub.execute_input":"2023-01-13T15:59:19.85536Z","iopub.status.idle":"2023-01-13T15:59:19.874777Z","shell.execute_reply.started":"2023-01-13T15:59:19.855324Z","shell.execute_reply":"2023-01-13T15:59:19.874023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Forming CC - MLO sample pairs","metadata":{}},{"cell_type":"markdown","source":"For most of the patients, scans with two different views have been performend (CC and MLO as depicted earlier). Thus, feeding the model with the two views at the same time could be interresting as they correspond to the same patient. For this purpose, pairs of analysis are formed for each patients.","metadata":{}},{"cell_type":"code","source":"def merge_CC_and_MLO(df, laterality):\n\n    df_cc = df.loc[(df_train['laterality'] == laterality)&(df_train['view'] == 'CC'),\n                   ['patient_id','age','implant','cancer', 'image_id']]\n    df_cc.rename(columns = {'image_id': 'CC_id'}, inplace = True)\n    \n    df_mlo = df.loc[(df_train['laterality'] == laterality)&(df_train['view'] == 'MLO'),\n                    ['patient_id', 'image_id']]\n    df_mlo.rename(columns = {'image_id': 'MLO_id'}, inplace = True)\n    \n    return pd.merge(df_cc, df_mlo, on = 'patient_id')","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.876191Z","iopub.execute_input":"2023-01-13T15:59:19.876453Z","iopub.status.idle":"2023-01-13T15:59:19.884231Z","shell.execute_reply.started":"2023-01-13T15:59:19.876421Z","shell.execute_reply":"2023-01-13T15:59:19.883416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# The samples of both latterality are merged depending on view\ndf_L = merge_CC_and_MLO(df_train, 'L')\ndf_R = merge_CC_and_MLO(df_train, 'R')\n\n# The samples form both latteratily are concatenated\ndf_train = pd.concat([df_L, df_R], ignore_index = True)","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.885341Z","iopub.execute_input":"2023-01-13T15:59:19.885661Z","iopub.status.idle":"2023-01-13T15:59:19.950308Z","shell.execute_reply.started":"2023-01-13T15:59:19.885626Z","shell.execute_reply":"2023-01-13T15:59:19.949633Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.951302Z","iopub.execute_input":"2023-01-13T15:59:19.951551Z","iopub.status.idle":"2023-01-13T15:59:19.961876Z","shell.execute_reply.started":"2023-01-13T15:59:19.95152Z","shell.execute_reply":"2023-01-13T15:59:19.961159Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.shape","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.965445Z","iopub.execute_input":"2023-01-13T15:59:19.965905Z","iopub.status.idle":"2023-01-13T15:59:19.976938Z","shell.execute_reply.started":"2023-01-13T15:59:19.965868Z","shell.execute_reply":"2023-01-13T15:59:19.976117Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Sampling positive and negative samples","metadata":{}},{"cell_type":"markdown","source":"The EDA revealed that only a bit more than 2% of the sample where positive. Having such a low amount of positive result could hinder the learning process of the neural network (hindering learning and encouraging overfitting).","metadata":{}},{"cell_type":"markdown","source":"**Oversampling positive samples**","metadata":{}},{"cell_type":"code","source":"df_train['cancer'].sum()/df_train.shape[0]","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.979009Z","iopub.execute_input":"2023-01-13T15:59:19.979222Z","iopub.status.idle":"2023-01-13T15:59:19.988449Z","shell.execute_reply.started":"2023-01-13T15:59:19.979198Z","shell.execute_reply":"2023-01-13T15:59:19.987392Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def oversampling(df, positive_ratio = 1):\n    neg = df[df['cancer'] == 0]\n    pos = df[df['cancer'] == 1]\n    \n    # Number of desired positive sample corresponding to the ratio\n    pos_num = neg.shape[0]*positive_ratio\n    \n    # Number of concat iteration for increasing sufficiently the nu\n    iter_num = (pos_num-pos.shape[0])//pos.shape[0]\n    \n    for i in range(iter_num):\n        df = pd.concat([df, pos], ignore_index = True)\n    \n    \n    # Recalculate the ratio\n    neg = df[df['cancer'] == 0]\n    pos = df[df['cancer'] == 1]\n    print(f'New ratio {pos.shape[0]/neg.shape[0]}')\n    \n    return df","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.990205Z","iopub.execute_input":"2023-01-13T15:59:19.990507Z","iopub.status.idle":"2023-01-13T15:59:19.996898Z","shell.execute_reply.started":"2023-01-13T15:59:19.990473Z","shell.execute_reply":"2023-01-13T15:59:19.996159Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train_over = oversampling(df_train, 1)","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:19.998299Z","iopub.execute_input":"2023-01-13T15:59:19.998853Z","iopub.status.idle":"2023-01-13T15:59:20.12454Z","shell.execute_reply.started":"2023-01-13T15:59:19.998806Z","shell.execute_reply":"2023-01-13T15:59:20.123798Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train_over.shape","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:20.126022Z","iopub.execute_input":"2023-01-13T15:59:20.126546Z","iopub.status.idle":"2023-01-13T15:59:20.132221Z","shell.execute_reply.started":"2023-01-13T15:59:20.126492Z","shell.execute_reply":"2023-01-13T15:59:20.131464Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 1.2. Tensorflow dataset","metadata":{}},{"cell_type":"markdown","source":"For improved performances, the data can be fed to the model as a tensorflow dataset. The function for creating the dataset was inspired of <a href=https://www.tensorflow.org/tutorials/load_data/images#using_tfdata_for_finer_control> TensorFlow documentation </a>.","metadata":{}},{"cell_type":"code","source":"# Import images in the right format\ndef decode_img(file_path):\n    '''\n    Import images in the right format and size\n    '''\n    # Load the raw data from the file as a string\n    img = tf.io.read_file(file_path)\n    \n    # Convert the compressed string to a 3D uint8 tensor\n    img = tf.io.decode_png(contents = img, channels=3)\n    \n    # Resize the image to the desired size\n    return img\n\ndef resize_img(img):\n    return tf.image.resize(img, [img_height, img_width])\n\ndef pad_img(img):\n    return tf.image.resize_with_pad(img, img_height, img_width)\n\ndef configure_for_performance(ds):\n    '''\n    Improve dataset performance through shuffuling, batching\n    and prefetching\n    '''\n    AUTOTUNE = tf.data.AUTOTUNE\n    ds = ds.cache()\n    ds = ds.shuffle(buffer_size=100000)\n    ds = ds.batch(batch_size)\n    ds = ds.prefetch(buffer_size=AUTOTUNE)\n    return ds","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:23.8447Z","iopub.execute_input":"2023-01-13T15:59:23.844985Z","iopub.status.idle":"2023-01-13T15:59:23.851994Z","shell.execute_reply.started":"2023-01-13T15:59:23.844956Z","shell.execute_reply":"2023-01-13T15:59:23.851301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def import_scans(df, col_image_id, padding = False):\n    # Find all the files paths\n    file_list = []\n    for patient, image in zip(df['patient_id'], df[col_image_id]):\n        file_list.append(f\"/kaggle/input/rsna-cropped-png-1024/cropped_dataset/{patient}/{image}.png\")\n\n    # load the images\n    # Set `num_parallel_calls` so multiple images are loaded/processed in parallel.\n    AUTOTUNE = tf.data.AUTOTUNE\n    ds_img = tf.data.Dataset.from_tensor_slices(file_list)\n    ds_img = ds_img.map(decode_img, num_parallel_calls=AUTOTUNE)\n    \n    # The image a resized with padding or not\n    if padding:\n        ds_img = ds_img.map(lambda x: pad_img(x), num_parallel_calls=AUTOTUNE)\n    else:\n        ds_img = ds_img.map(resize_img, num_parallel_calls=AUTOTUNE)\n    \n    ds_label = tf.data.Dataset.from_tensor_slices(df['cancer'])\n    \n    # Create the img, label pair with zip function\n    ds_full = tf.data.Dataset.zip((ds_img, ds_label))\n    \n    \n    # The dataset is split between a train and validation set\n    val_size = int(len(file_list) * 0.1)\n    ds_train = ds_full.skip(val_size)\n    ds_val = ds_full.take(val_size)\n\n    print(f'train set created with {tf.data.experimental.cardinality(ds_train).numpy()} elements')\n    print(f'val set created with {tf.data.experimental.cardinality(ds_val).numpy()} elements')\n    \n    \n    # Now that the dataset have been created, it can be optimized for performances. \n    # The dataset is shuffled, batched and batches are prefetched to accelerate processing.\n    ds_train = configure_for_performance(ds_train)\n    ds_val = configure_for_performance(ds_val)\n    \n    return ds_train, ds_val","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:24.535858Z","iopub.execute_input":"2023-01-13T15:59:24.536767Z","iopub.status.idle":"2023-01-13T15:59:24.545993Z","shell.execute_reply.started":"2023-01-13T15:59:24.536729Z","shell.execute_reply":"2023-01-13T15:59:24.545264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"img_height = 224\nimg_width = 224\n\nbatch_size = 16\n\ndf_sample = df_train_over.sample(6000)\n\nds_train_cc, ds_val_cc = import_scans(df_sample,'CC_id', padding = True)\n\nds_train_mlo, ds_val_mlo = import_scans(df_sample,'MLO_id', padding = True)","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:26.292274Z","iopub.execute_input":"2023-01-13T15:59:26.293087Z","iopub.status.idle":"2023-01-13T15:59:28.555018Z","shell.execute_reply.started":"2023-01-13T15:59:26.293051Z","shell.execute_reply":"2023-01-13T15:59:28.554148Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ds_train_full = ds_train_cc.concatenate(ds_train_mlo)\nds_val_full = ds_val_cc.concatenate(ds_val_mlo)","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:31.164812Z","iopub.execute_input":"2023-01-13T15:59:31.165087Z","iopub.status.idle":"2023-01-13T15:59:31.172734Z","shell.execute_reply.started":"2023-01-13T15:59:31.165058Z","shell.execute_reply":"2023-01-13T15:59:31.171947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 2. CNN","metadata":{}},{"cell_type":"markdown","source":"## 2.1. CNN from Scratch","metadata":{}},{"cell_type":"markdown","source":"Once converted into a tensorflow dataset, the data can be fed to a neural network. Computer vision is often performed with convolutive neural networks (CNN). The structure of the convolutive network that have been built and studied is this project is summarised in the following figure :\n\nThe blue layer corresponds to an augmentation layer\n\n![image.png](attachment:71d80109-35f0-4fc9-924d-1f55041f1f7e.png)","metadata":{},"attachments":{"71d80109-35f0-4fc9-924d-1f55041f1f7e.png":{"image/png":"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"}}},{"cell_type":"code","source":"# Augmentation is performed with keras layers\ndata_augmentation = tf.keras.Sequential([\n    RandomFlip(),\n    RandomRotation(0.1, fill_mode= 'constant'),\n    RandomZoom((0.1, -0.3), (0.1, -0.3), fill_mode= 'constant'),\n    RandomTranslation(0.2,0.2, fill_mode= 'constant')\n])","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:37.65423Z","iopub.execute_input":"2023-01-13T15:59:37.654841Z","iopub.status.idle":"2023-01-13T15:59:37.998568Z","shell.execute_reply.started":"2023-01-13T15:59:37.654806Z","shell.execute_reply":"2023-01-13T15:59:37.997783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The effect of the transformation layer on a scan is given in the following figure :","metadata":{}},{"cell_type":"code","source":"for img, label in ds_train_full.take(1):\n    ax = plt.subplot(121)\n    ax.imshow(img[0].numpy().astype('uint8'))\n    \n    ax2 = plt.subplot(122)\n    ax2.imshow(data_augmentation(img)[0].numpy().astype('uint8'))","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:59:40.224255Z","iopub.execute_input":"2023-01-13T15:59:40.22505Z","iopub.status.idle":"2023-01-13T16:00:24.84525Z","shell.execute_reply.started":"2023-01-13T15:59:40.225014Z","shell.execute_reply":"2023-01-13T16:00:24.844565Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if not os.path.isdir('model_2.model'):\n    '''\n    Training the new model\n    '''\n    \n    model_2 =  Sequential([\n        data_augmentation,\n\n        Conv2D(64, (3,3), # Block 1\n               input_shape=(img_height, img_width, 3),\n               padding = 'same',\n               activation = 'relu'),\n        MaxPooling2D(pool_size = (2,2),\n                     strides = (2,2)),\n        Conv2D(128, (3,3), # Block 2\n               padding = 'same',\n               activation = 'relu'),\n        MaxPooling2D(pool_size = (2,2),\n                     strides = (2,2)),\n        Conv2D(256, (3,3), # Block 3\n               input_shape=(img_height, img_width, 3),\n               padding = 'same',\n               activation = 'relu'),\n        MaxPooling2D(pool_size = (2,2),\n                     strides = (2,2)),\n        Conv2D(512, (3,3), # Block 4\n               input_shape=(img_height, img_width, 3),\n               padding = 'same',\n               activation = 'relu'),\n        MaxPooling2D(pool_size = (2,2),\n                     strides = (2,2)),\n        Conv2D(512, (3,3), # Block 5\n               input_shape=(img_height, img_width, 3),\n               padding = 'same',\n               activation = 'relu'),\n        MaxPooling2D(pool_size = (2,2),\n                     strides = (2,2)),\n        Flatten(), # Flatten\n        Dense(512, activation ='relu'), # Dense\n        Dense(1, activation ='sigmoid') # Output\n    ])\n\n    model_2.build(input_shape = tuple(ds_train_full._flat_shapes[0]))\n    model_2.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n                optimizer=tf.keras.optimizers.Adam(learning_rate = 1e-5, decay = 1e-6),\n                metrics=[tf.keras.metrics.AUC()])\n    \n    epochs = 5\n\n    history = model_2.fit(ds_train_full,\n                      validation_data = ds_val_full,\n                      epochs=epochs)\n    model_2.save('model_2.model')\n    print('model trained and saved')\n\nelse:\n    '''\n    Only loading the allready trained model\n    '''\n    model_2 = keras.models.load_model('model_2.model')\n    print('model loaded')","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:02:41.220432Z","iopub.execute_input":"2023-01-13T16:02:41.221168Z","iopub.status.idle":"2023-01-13T16:06:02.068583Z","shell.execute_reply.started":"2023-01-13T16:02:41.221134Z","shell.execute_reply":"2023-01-13T16:06:02.066988Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_2.summary()","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:06:16.915527Z","iopub.execute_input":"2023-01-13T16:06:16.915804Z","iopub.status.idle":"2023-01-13T16:06:16.927563Z","shell.execute_reply.started":"2023-01-13T16:06:16.915777Z","shell.execute_reply":"2023-01-13T16:06:16.926843Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# dict_keys(['loss', 'auc_1', 'val_loss', 'val_auc_1'])\n\nplt.plot(history.history['loss'], color = c_0)\nplt.plot(history.history['val_loss'], color = c_1)\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.legend(['Train loss', 'Val loss'])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:06:20.99861Z","iopub.execute_input":"2023-01-13T16:06:20.998889Z","iopub.status.idle":"2023-01-13T16:06:21.239825Z","shell.execute_reply.started":"2023-01-13T16:06:20.998861Z","shell.execute_reply":"2023-01-13T16:06:21.239096Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.plot(history.history['auc'], color = c_0)\nplt.plot(history.history['val_auc'], color = c_1)\nplt.xlabel('Epochs')\nplt.ylabel('AUC')\nplt.legend(['Train AUC', 'Val AUC'])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:06:45.199833Z","iopub.execute_input":"2023-01-13T16:06:45.200115Z","iopub.status.idle":"2023-01-13T16:06:45.439914Z","shell.execute_reply.started":"2023-01-13T16:06:45.200086Z","shell.execute_reply":"2023-01-13T16:06:45.439226Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2.2. CNN from transfer learning","metadata":{}},{"cell_type":"code","source":"xception = tf.keras.applications.Xception(include_top=False,\n                                          weights=\"imagenet\",\n                                          input_tensor=None,\n                                          input_shape=(224, 224, 3),\n                                          pooling=None,\n                                          classes=['cancer'],\n                                          classifier_activation=\"softmax\")\n\nxception.summary()","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:08:11.447254Z","iopub.execute_input":"2023-01-13T16:08:11.447754Z","iopub.status.idle":"2023-01-13T16:08:12.584813Z","shell.execute_reply.started":"2023-01-13T16:08:11.44772Z","shell.execute_reply":"2023-01-13T16:08:12.58406Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"input_xception = keras.layers.Lambda(lambda x: tf.keras.applications.xception.preprocess_input(x))","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:08:26.52986Z","iopub.execute_input":"2023-01-13T16:08:26.530131Z","iopub.status.idle":"2023-01-13T16:08:26.535504Z","shell.execute_reply.started":"2023-01-13T16:08:26.530103Z","shell.execute_reply":"2023-01-13T16:08:26.534657Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"xception = Sequential([\n    data_augmentation,\n    input_xception,\n    xception,\n    Flatten(), # Flatten\n    Dense(512, activation ='relu'), # Dense 1\n    BatchNormalization(),\n    Dense(1, activation ='sigmoid') # Output\n])\nxception.layers[2].trainable = False\nxception.build(input_shape = tuple(ds_train_full._flat_shapes[0]))\nxception.summary()","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:08:27.928734Z","iopub.execute_input":"2023-01-13T16:08:27.929021Z","iopub.status.idle":"2023-01-13T16:08:28.480049Z","shell.execute_reply.started":"2023-01-13T16:08:27.928992Z","shell.execute_reply":"2023-01-13T16:08:28.479031Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"xception.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n            optimizer=tf.keras.optimizers.Adam(learning_rate = 1e-5, decay = 1e-6),\n            metrics=[tf.keras.metrics.AUC()])\n\nepochs = 20\n\nhistory = xception.fit(ds_train_full,\n                  validation_data = ds_val_full,\n                  epochs=epochs)\nxception.save('xception_trained')","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:08:35.141834Z","iopub.execute_input":"2023-01-13T16:08:35.142127Z","iopub.status.idle":"2023-01-13T16:12:16.048009Z","shell.execute_reply.started":"2023-01-13T16:08:35.142096Z","shell.execute_reply":"2023-01-13T16:12:16.04711Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.plot(history.history['loss'], color = c_0)\nplt.plot(history.history['val_loss'], color = c_1)\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.legend(['Train loss', 'Val loss'])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:13:09.978062Z","iopub.execute_input":"2023-01-13T16:13:09.978666Z","iopub.status.idle":"2023-01-13T16:13:10.209995Z","shell.execute_reply.started":"2023-01-13T16:13:09.978629Z","shell.execute_reply":"2023-01-13T16:13:10.209268Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.plot(history.history['auc_1'], color = c_0)\nplt.plot(history.history['val_auc_1'], color = c_1)\nplt.xlabel('Epochs')\nplt.ylabel('AUC')\nplt.legend(['Train AUC', 'Val AUC'])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-01-13T16:13:51.943127Z","iopub.execute_input":"2023-01-13T16:13:51.943422Z","iopub.status.idle":"2023-01-13T16:13:52.175132Z","shell.execute_reply.started":"2023-01-13T16:13:51.943392Z","shell.execute_reply":"2023-01-13T16:13:52.174339Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def evaluate_model(y_true, y_pred):\n    recall = tf.keras.metrics.Recall()\n    recall.update_state(y_true, y_pred)\n    recall = recall.result().numpy()\n    \n    precision = tf.keras.metrics.Precision()\n    precision.update_state(y_true, y_pred)\n    precision = precision.result().numpy()\n    \n    f1_score = 2 * precision * recall / (precision + recall)\n    \n    return recall, precision, f1_score","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:15:22.264216Z","iopub.execute_input":"2023-01-13T15:15:22.264773Z","iopub.status.idle":"2023-01-13T15:15:22.273224Z","shell.execute_reply.started":"2023-01-13T15:15:22.264725Z","shell.execute_reply":"2023-01-13T15:15:22.272336Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_true = []\ny_pred = []\n\ndf_eval = df_train_over.drop(df_sample.index).sample(1000)\n\nds_eval_cc, ds_val_cc = import_scans(df_eval,'CC_id', padding = True)\n\nds_eval_mlo, ds_val_mlo = import_scans(df_eval,'MLO_id', padding = True)\n\nds_eval = ds_eval_cc.concatenate(ds_eval_mlo)\n\nfor batch_img, batch_label in ds_eval:\n    # Add y_true\n    y_true.extend(batch_label.numpy())\n    \n    # predict y_pred\n    prediction =  xception.predict(batch_img)\n    y_pred.extend(prediction)","metadata":{"execution":{"iopub.status.busy":"2023-01-13T15:21:57.155999Z","iopub.execute_input":"2023-01-13T15:21:57.1568Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy = tf.keras.metrics.BinaryAccuracy()\naccuracy.update_state(y_true, y_pred)\naccuracy = accuracy.result().numpy()\nprint(f'accuracy on validation set is {accuracy}')","metadata":{"execution":{"iopub.status.busy":"2023-01-13T13:52:27.741176Z","iopub.execute_input":"2023-01-13T13:52:27.741476Z","iopub.status.idle":"2023-01-13T13:52:27.779654Z","shell.execute_reply.started":"2023-01-13T13:52:27.741438Z","shell.execute_reply":"2023-01-13T13:52:27.778917Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"recall, precision, f1_score = evaluate_model(y_true, y_pred)\nprint(f'Recall is : {recall}')\nprint(f'Precision is : {precision}')\nprint(f'f1_score is : {f1_score}')","metadata":{"execution":{"iopub.status.busy":"2023-01-13T13:52:27.780976Z","iopub.execute_input":"2023-01-13T13:52:27.781236Z","iopub.status.idle":"2023-01-13T13:52:27.81724Z","shell.execute_reply.started":"2023-01-13T13:52:27.781203Z","shell.execute_reply":"2023-01-13T13:52:27.815995Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2.2. CNN from transfer learning","metadata":{}},{"cell_type":"markdown","source":"# 3. Example of result Submission","metadata":{}},{"cell_type":"markdown","source":"A clear process with dedicated functions is created for converting the samples to the right format before prediction.\n\n**The preprocessing before submission is summarised here :**\n\n\n![image888.png](attachment:b8fb373b-2aa6-4fdf-bdaa-50cb31d1be5a.png)","metadata":{},"attachments":{"b8fb373b-2aa6-4fdf-bdaa-50cb31d1be5a.png":{"image/png":"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"}}},{"cell_type":"code","source":"'''\n------------------------------------------------------------\nImages are cropped for centering the object of interest (in a second step)\n------------------------------------------------------------\n'''\n    \ndef get_boundaries(data, threshold = 0.008):\n    '''\n    Return the lower and upper boundaries as a list\n    [lower, upper]\n    given a threshold (default = 0.8%)\n    '''\n    s_data = np.cumsum(data)/data.sum()\n    lower = len(s_data[s_data<= threshold])\n    upper = len(s_data[s_data<=(1- threshold)])\n    \n    return [lower, upper]\n\ndef img_crop(img):\n    '''\n    Find the upper and lower boundaries\n    of an object of interest in the picture\n    and crop the picture accordingly\n    '''\n    lb_x, ub_x = get_boundaries(img.sum(axis = 0))\n    lb_y, ub_y = get_boundaries(img.sum(axis = 1), threshold = 0.01)\n    \n    return img[lb_y:ub_y,lb_x:ub_x]\n\n\n'''\n----------------------------------------------\nFirst the dicom images are converted to png\n----------------------------------------------\n'''\n\ndef convert_png(path, size=1024, save_folder=\"output/\", extension=\"png\"):\n    '''\n    Convert dicom images into png based on image path\n    '''\n    # Get patient_id and image id\n    patient = path.split('/')[-2]\n    image = path.split('/')[-1][:-4]\n    \n    # The scan is read and windiwing is applied\n    dicom = pydicom.dcmread(path)\n    img = apply_voi_lut(dicom.pixel_array, dicom)\n    \n    # The image is rescaled between 0 and 1\n    img = (img - img.min()) / (img.max() - img.min())\n    \n    # If the scan is negative, the value are inverted\n    if dicom.PhotometricInterpretation == \"MONOCHROME1\":\n        img = 1 - img\n    \n    # Resize the scan to a png\n    img = cv2.resize(img, (size, size))\n    \n    img = (img * 255).astype(np.uint8)\n    \n    # Save the image (255 based)   \n    save_path = (f\"/kaggle/working/\" + str(patient))\n    \n    if not os.path.isdir(save_path):\n        os.mkdir(save_path)\n            \n    img = img_crop(img)\n    cv2.imwrite(f'{save_path}/{image}.png', img)\n\n\n'''\n------------------------------------------------------\nThe dataset containing the images and label is created\n------------------------------------------------------\n'''\n\ndef create_submit_ds(df, col_image_id, padding = False):\n    patient = df['patient_id']\n    image = df['image_id']\n    # Find all the files paths\n    file_path = [f\"/kaggle/working/{patient}/{image}.png\"]\n    \n    # load the image\n    ds_img = tf.data.Dataset.from_tensor_slices(file_path)\n    ds_img = ds_img.map(decode_img)\n    \n    # The image a resized with padding or not\n    if padding:\n        ds_img = ds_img.map(lambda x: pad_img(x))\n    else:\n        ds_img = ds_img.map(resize_img)\n        \n    ds_img = ds_img.batch(batch_size)\n    return ds_img","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test = pd.read_csv('/kaggle/input/rsna-breast-cancer-detection/test.csv')\n\npredictions = []\nfor index, patient in df_test.iterrows():\n    dicom_path = f\"/kaggle/input/rsna-breast-cancer-detection/test_images/{patient['patient_id']}/{patient['image_id']}.dcm\"\n    convert_png(dicom_path)\n    ds = create_submit_ds(patient, 'image_id')\n    pred = model_2.predict(ds)\n    predictions.append(pred[0][0])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submit_sample = pd.read_csv('/kaggle/input/rsna-breast-cancer-detection/sample_submission.csv')\nsubmit_sample.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission = pd.DataFrame(data={'prediction_id': df_test['prediction_id'], 'cancer': predictions}).groupby('prediction_id').mean().reset_index()\nsubmission.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('submission.csv', index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}