{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":11848,"databundleVersionId":862157,"sourceType":"competition"},{"sourceId":212102090,"sourceType":"kernelVersion"},{"sourceId":212121692,"sourceType":"kernelVersion"},{"sourceId":212122809,"sourceType":"kernelVersion"}],"dockerImageVersionId":30804,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"##### Histopathologic Cancer Detection Project Summary\n##### Author: Aaron Storey\n##### Date: December 09, 2024\n##### Version: 1.0\n\nThis notebook provides a comprehensive summary of the Histopathologic Cancer Detection project,\nincluding links to all notebooks, experimental results, and key learnings across 8 weeks.","metadata":{}},{"cell_type":"markdown","source":"## Project Overview","metadata":{}},{"cell_type":"markdown","source":"### Objective\nDevelop a deep learning model to detect metastatic cancer in small image patches taken from larger digital pathology scans.","metadata":{}},{"cell_type":"markdown","source":"### Dataset\n- Training images: 220,025\n- Test images: 57,458\n- Image size: 96x96 pixels\n- Classes: Binary (0: Benign, 1: Malignant)\n- Source: Kaggle Competition","metadata":{}},{"cell_type":"markdown","source":"## Repository Structure","metadata":{}},{"cell_type":"markdown","source":"### Core Notebooks\n1. [EDA Notebook](https://www.kaggle.com/code/astoreyai/as-histopathologic-cancer-01-eda)\n    - Data distribution analysis\n    - Image visualization\n    - WSI grouping analysis\n    - Quality checks\n\n2. [Training Notebook](https://www.kaggle.com/code/astoreyai/as-histopathologic-cancer-02-training)\n    - Model architecture\n    - Training pipeline\n    - Performance metrics\n    - Visualization\n\n3. [Testing Notebook](https://www.kaggle.com/code/astoreyai/as-histopathologic-cancer-03-testing)\n    - Inference pipeline\n    - Test-time augmentation\n    - Submission generation","metadata":{}},{"cell_type":"markdown","source":"## Experimental Summary","metadata":{}},{"cell_type":"markdown","source":"### Week 1: Baseline Implementation\n - Model: Basic CNN\n - Score: 0.54\n - Key Learnings:\n   * Basic preprocessing pipeline established\n   * Identified class imbalance challenges\n   * Initial data augmentation implementation\n\n### Week 2-3: ResNet Architecture\n - Model: ResNet-50\n - Best Score: 0.7855\n - Improvements:\n   * Implemented GroupShuffleSplit for WSI validation\n   * Added Focal Loss\n   * Enhanced data augmentation\n   * Implemented CyclicLR scheduler\n\n\n### Week 4-5: EfficientNet Implementation\n - Model: EfficientNet-B0\n - Best Score: 0.8682 (V13)\n - Key Features:\n   * Advanced preprocessing pipeline\n   * Stain normalization\n   * Optimized batch size and learning rate\n   * Comprehensive metrics tracking\n\n### Week 6-7: U-Net Exploration\n - Model: Modified U-Net\n - Score: 0.8082 (V20)\n - Innovations:\n   * Balanced sampling implementation\n   * Global average pooling adaptation\n   * Enhanced augmentation pipeline\n\n### Week 8: Final Optimizations\n - Model: Pseudo-Labeled EfficientNet-B0 (PL-V6)\n - Final Score: 0.8230\n - Implementation:\n   * Test-time augmentation\n   * Optimized inference pipeline\n   * Comprehensive documentation","metadata":{}},{"cell_type":"markdown","source":"## Performance Summary","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\n\n# Create performance summary dataframe\nperformance_data = {\n    'Week': [1, 2, 4, 6, 8],\n    'Architecture': ['Basic CNN', 'ResNet-50', 'EfficientNet-B0', 'U-Net', 'Pseudo-labeled EfficientNet-B0'],\n    'Score': [0.54, 0.7855, 0.8682, 0.8082, 0.8230],\n    'Version': ['V1', 'V6', 'V13', 'V20', 'PL-V6 (Final)']\n}\n\ndf = pd.DataFrame(performance_data)\nprint(\"Performance Summary:\")\nprint(df.to_string(index=False))\n\n# Plot performance evolution\nplt.figure(figsize=(10, 6))\nplt.plot(df['Week'], df['Score'], 'bo-')\nplt.title('Model Performance Evolution')\nplt.xlabel('Week')\nplt.ylabel('Score')\nplt.grid(True)\nplt.show()","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-09T17:29:33.435157Z","iopub.execute_input":"2024-12-09T17:29:33.435650Z","iopub.status.idle":"2024-12-09T17:29:33.738745Z","shell.execute_reply.started":"2024-12-09T17:29:33.435533Z","shell.execute_reply":"2024-12-09T17:29:33.737613Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Key Learnings and Best Practices","metadata":{}},{"cell_type":"markdown","source":"### Data Processing\n 1. WSI-based validation crucial for realistic performance\n 2. Stain normalization impacts model generalization\n 3. Balanced sampling improves training stability\n 4. Advanced augmentation strategies necessary\n\n### Model Architecture\n 1. EfficientNet-B0 optimal for the task\n 2. Custom head design important\n 3. Proper learning rate critical\n 4. Batch size optimization necessary\n\n### Training Strategy\n 1. Early stopping prevents overfitting\n 2. Learning rate scheduling improves convergence\n 3. Test-time augmentation boosts performance\n 4. Regular validation checks essential","metadata":{}},{"cell_type":"markdown","source":"## Future Recommendations","metadata":{}},{"cell_type":"markdown","source":"### Immediate Improvements\n 1. Ensemble top-performing models\n 2. Implement cross-validation\n 3. Explore advanced data augmentation\n\n### Long-term Development\n 1. Investigate semi-supervised approaches\n 2. Implement multi-scale feature integration\n 3. Explore attention mechanisms\n 4. Develop automated preprocessing pipeline","metadata":{"execution":{"iopub.status.busy":"2024-12-09T17:29:13.327365Z","iopub.execute_input":"2024-12-09T17:29:13.327820Z","iopub.status.idle":"2024-12-09T17:29:13.335587Z","shell.execute_reply.started":"2024-12-09T17:29:13.327783Z","shell.execute_reply":"2024-12-09T17:29:13.333872Z"}}},{"cell_type":"markdown","source":"## Requirements and Dependencies","metadata":{}},{"cell_type":"code","source":"requirements = \"\"\"\ntorch>=1.9.0\nalbumentations>=1.0.3\nefficientnet_pytorch>=0.7.1\nopencv-python>=4.5.3\nnumpy>=1.21.2\npandas>=1.3.3\nscikit-learn>=0.24.2\nmatplotlib>=3.4.3\ntqdm>=4.62.3\n\"\"\"\n\nwith open('requirements.txt', 'w') as f:\n    f.write(requirements)\n\nprint(\"Requirements saved to 'requirements.txt'\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T17:29:33.740607Z","iopub.execute_input":"2024-12-09T17:29:33.740952Z","iopub.status.idle":"2024-12-09T17:29:33.748010Z","shell.execute_reply.started":"2024-12-09T17:29:33.740917Z","shell.execute_reply":"2024-12-09T17:29:33.746840Z"}},"outputs":[],"execution_count":null}]}