{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":29653,"databundleVersionId":2420395,"sourceType":"competition"},{"sourceId":76358302,"sourceType":"kernelVersion"},{"sourceId":44065,"sourceType":"modelInstanceVersion","modelInstanceId":37004,"modelId":51947}],"dockerImageVersionId":30132,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"References:\nhttps://www.kaggle.com/ammarnassanalhajali/brain-tumor-3d-training\nhttps://www.kaggle.com/code/vexxingbanana/simple-pytorch-cnn","metadata":{}},{"cell_type":"code","source":"# Install dependencies\n!pip install -q pydicom tensorflow\n\n# Imports\nimport os, cv2, pydicom\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import f1_score, cohen_kappa_score, roc_auc_score, roc_curve, auc\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\n# === Load and preprocess MRI slices ===\nlabels_df = pd.read_csv('/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')\ntrain_path = '/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/train/'","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-11T19:37:57.613174Z","iopub.execute_input":"2025-05-11T19:37:57.613545Z","iopub.status.idle":"2025-05-11T19:38:05.668932Z","shell.execute_reply.started":"2025-05-11T19:37:57.613509Z","shell.execute_reply":"2025-05-11T19:38:05.667769Z"}},"outputs":[{"name":"stdout","text":"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"def load_image(patient_id, img_size=(128, 128)):\n    folder = os.path.join(train_path, str(patient_id).zfill(5), \"T1w\")\n    if not os.path.exists(folder): return None\n    files = sorted(os.listdir(folder))\n    if len(files) == 0: return None\n    path = os.path.join(folder, files[len(files)//2])\n    dcm = pydicom.dcmread(path)\n    img = dcm.pixel_array\n    img = cv2.resize(img, img_size)\n    img = img / 255.0\n    return np.expand_dims(img, -1)\n\nX, y = [], []\nfor _, row in labels_df.iterrows():\n    img = load_image(row['BraTS21ID'])\n    if img is not None:\n        X.append(img)\n        y.append(row['MGMT_value'])\n\nX = np.array(X)\ny = np.array(y)\n\n# === Train/Val/Test Split ===\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, stratify=y_train, random_state=42)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-11T19:38:05.672135Z","iopub.execute_input":"2025-05-11T19:38:05.672601Z","iopub.status.idle":"2025-05-11T19:38:08.863985Z","shell.execute_reply.started":"2025-05-11T19:38:05.67254Z","shell.execute_reply":"2025-05-11T19:38:08.862471Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"# Define Vision Transformer \ninput_shape = (128, 128, 1)\npatch_size = 16\nnum_patches = (128 // patch_size) ** 2\nprojection_dim = 64\nnum_heads = 4\ntransformer_units = [projection_dim * 2, projection_dim]\ntransformer_layers = 4\nmlp_head_units = [128, 64]\ndropout_rate = 0.1","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-11T19:38:08.865398Z","iopub.execute_input":"2025-05-11T19:38:08.865793Z","iopub.status.idle":"2025-05-11T19:38:08.871867Z","shell.execute_reply.started":"2025-05-11T19:38:08.865747Z","shell.execute_reply":"2025-05-11T19:38:08.870886Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"class PatchEncoder(layers.Layer):\n    def __init__(self, num_patches, projection_dim):\n        super().__init__()\n        self.projection = layers.Dense(units=projection_dim)\n        self.position_embedding = layers.Embedding(input_dim=num_patches, output_dim=projection_dim)\n\n    def call(self, patch):\n        positions = tf.range(start=0, limit=num_patches, delta=1)\n        encoded = self.projection(patch) + self.position_embedding(positions)\n        return encoded\n\ndef mlp(x, hidden_units, dropout_rate):\n    for units in hidden_units:\n        x = layers.Dense(units, activation=tf.nn.gelu)(x)\n        x = layers.Dropout(dropout_rate)(x)\n    return x\n\ndef build_vit_classifier():\n    inputs = layers.Input(shape=input_shape)\n    patches = layers.Conv2D(filters=projection_dim, kernel_size=patch_size, strides=patch_size)(inputs)\n    x = layers.Reshape((num_patches, -1))(patches)\n    x = PatchEncoder(num_patches, projection_dim)(x)\n\n    for _ in range(transformer_layers):\n        x1 = layers.LayerNormalization(epsilon=1e-6)(x)\n        attention_output = layers.MultiHeadAttention(num_heads=num_heads, key_dim=projection_dim)(x1, x1)\n        x2 = layers.Add()([attention_output, x])\n        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\n        x3 = mlp(x3, transformer_units, dropout_rate)\n        x = layers.Add()([x3, x2])\n\n    x = layers.LayerNormalization(epsilon=1e-6)(x)\n    x = layers.GlobalAveragePooling1D()(x)\n    x = mlp(x, mlp_head_units, dropout_rate)\n    outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n    return keras.Model(inputs, outputs)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-11T19:38:08.874279Z","iopub.execute_input":"2025-05-11T19:38:08.874597Z","iopub.status.idle":"2025-05-11T19:38:08.889323Z","shell.execute_reply.started":"2025-05-11T19:38:08.874566Z","shell.execute_reply":"2025-05-11T19:38:08.888329Z"}},"outputs":[],"execution_count":13},{"cell_type":"code","source":"# Compile and Train\nvit_model = build_vit_classifier()\nvit_model.compile(optimizer=tf.keras.optimizers.Adam(1e-4),\n                  loss=\"binary_crossentropy\",\n                  metrics=[\"accuracy\"])\n\nearly_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\nhistory = vit_model.fit(X_train, y_train,\n                        validation_data=(X_val, y_val),\n                        epochs=30,\n                        batch_size=16,\n                        callbacks=[early_stop],\n                        verbose=1)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-11T19:38:08.890825Z","iopub.execute_input":"2025-05-11T19:38:08.891237Z","iopub.status.idle":"2025-05-11T19:38:55.001371Z","shell.execute_reply.started":"2025-05-11T19:38:08.891184Z","shell.execute_reply":"2025-05-11T19:38:54.999955Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/30\n22/22 [==============================] - 10s 187ms/step - loss: 0.7062 - accuracy: 0.4801 - val_loss: 0.7027 - val_accuracy: 0.5214\nEpoch 2/30\n22/22 [==============================] - 3s 158ms/step - loss: 0.7036 - accuracy: 0.4556 - val_loss: 0.6916 - val_accuracy: 0.5043\nEpoch 3/30\n22/22 [==============================] - 3s 144ms/step - loss: 0.6996 - accuracy: 0.4305 - val_loss: 0.6927 - val_accuracy: 0.5299\nEpoch 4/30\n22/22 [==============================] - 3s 142ms/step - loss: 0.7026 - accuracy: 0.4500 - val_loss: 0.6890 - val_accuracy: 0.5214\nEpoch 5/30\n22/22 [==============================] - 3s 145ms/step - loss: 0.6954 - accuracy: 0.5391 - val_loss: 0.6893 - val_accuracy: 0.5214\nEpoch 6/30\n22/22 [==============================] - 3s 142ms/step - loss: 0.7002 - accuracy: 0.4826 - val_loss: 0.6900 - val_accuracy: 0.5299\nEpoch 7/30\n22/22 [==============================] - 3s 143ms/step - loss: 0.6898 - accuracy: 0.5457 - val_loss: 0.6882 - val_accuracy: 0.5214\nEpoch 8/30\n22/22 [==============================] - 3s 147ms/step - loss: 0.7064 - accuracy: 0.4791 - val_loss: 0.6968 - val_accuracy: 0.5214\nEpoch 9/30\n22/22 [==============================] - 3s 141ms/step - loss: 0.6864 - accuracy: 0.5747 - val_loss: 0.6938 - val_accuracy: 0.5214\nEpoch 10/30\n22/22 [==============================] - 3s 143ms/step - loss: 0.6920 - accuracy: 0.5366 - val_loss: 0.7038 - val_accuracy: 0.5214\nEpoch 11/30\n22/22 [==============================] - 3s 146ms/step - loss: 0.6913 - accuracy: 0.4970 - val_loss: 0.6913 - val_accuracy: 0.5299\nEpoch 12/30\n22/22 [==============================] - 3s 155ms/step - loss: 0.6920 - accuracy: 0.5707 - val_loss: 0.6900 - val_accuracy: 0.5214\n","output_type":"stream"}],"execution_count":14},{"cell_type":"code","source":"# Evaluate\ntest_loss, test_acc = vit_model.evaluate(X_test, y_test, verbose=0)\ny_pred_prob = vit_model.predict(X_test)\ny_pred = (y_pred_prob > 0.5).astype(int)\n\ntrain_acc = history.history['accuracy'][-1]\nval_acc = history.history['val_accuracy'][-1]\nf1 = f1_score(y_test, y_pred)\nkappa = cohen_kappa_score(y_test, y_pred)\nroc_auc = roc_auc_score(y_test, y_pred_prob)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-11T19:38:55.002916Z","iopub.execute_input":"2025-05-11T19:38:55.003221Z","iopub.status.idle":"2025-05-11T19:38:56.420897Z","shell.execute_reply.started":"2025-05-11T19:38:55.003188Z","shell.execute_reply":"2025-05-11T19:38:56.4199Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"print(f\"Training Accuracy: {train_acc:.4f}\")\nprint(f\"Validation Accuracy: {val_acc:.4f}\")\nprint(f\"Test Accuracy: {test_acc:.4f}\")\nprint(f\"F1 Score: {f1:.4f}\")\nprint(f\"Cohen's Kappa: {kappa:.4f}\")\nprint(f\"AUC: {roc_auc:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-11T19:38:56.422827Z","iopub.execute_input":"2025-05-11T19:38:56.42308Z","iopub.status.idle":"2025-05-11T19:38:56.430272Z","shell.execute_reply.started":"2025-05-11T19:38:56.423054Z","shell.execute_reply":"2025-05-11T19:38:56.429276Z"}},"outputs":[{"name":"stdout","text":"Training Accuracy: 0.5413\nValidation Accuracy: 0.5214\nTest Accuracy: 0.5214\nF1 Score: 0.6854\nCohen's Kappa: 0.0000\nAUC: 0.5682\n","output_type":"stream"}],"execution_count":16},{"cell_type":"code","source":"# Plots \nplt.figure(figsize=(14, 5))\nplt.subplot(1, 2, 1)\nplt.plot(history.history['accuracy'], label='Train Acc')\nplt.plot(history.history['val_accuracy'], label='Val Acc')\nplt.title('Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.legend()\n\nplt.subplot(1, 2, 2)\nplt.plot(history.history['loss'], label='Train Loss')\nplt.plot(history.history['val_loss'], label='Val Loss')\nplt.title('Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.legend()\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-11T19:38:56.43158Z","iopub.execute_input":"2025-05-11T19:38:56.431878Z","iopub.status.idle":"2025-05-11T19:38:56.962738Z","shell.execute_reply.started":"2025-05-11T19:38:56.431828Z","shell.execute_reply":"2025-05-11T19:38:56.96164Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1008x360 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}],"execution_count":17},{"cell_type":"code","source":"# ROC Curve \nfpr, tpr, _ = roc_curve(y_test, y_pred_prob)\nroc_val = auc(fpr, tpr)\n\nplt.figure()\nplt.plot(fpr, tpr, label=f'ROC (AUC = {roc_val:.2f})')\nplt.plot([0, 1], [0, 1], linestyle='--', color='gray')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('ROC Curve - ViT (New Method IV)')\nplt.legend()\nplt.grid()\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-11T19:38:56.964406Z","iopub.execute_input":"2025-05-11T19:38:56.964786Z","iopub.status.idle":"2025-05-11T19:38:57.23209Z","shell.execute_reply.started":"2025-05-11T19:38:56.964742Z","shell.execute_reply":"2025-05-11T19:38:57.230891Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}],"execution_count":18}]}