CAMELYON17 - Metastasis Detection

This medical AI research project aims to automatically detect breast cancer metastases in histopathological images (H&E staining) using the CAMELYON17 benchmark dataset. The system performs patch-level classification (224x224 pixels) with CNN architectures (ResNet, EfficientNet), then aggregates results at the patient level for pN-stage classification (pN0 to pN3). The project addresses multi-hospital domain shift (5 sources), class imbalance, and medical interpretability via Grad-CAM and attention visualization. Priority is given to sensitivity to minimize false negatives (missed tumors).

Challenges

• Managing domain shift across 5 source hospitals with different staining protocols
• Severe class imbalance between normal and tumor patches
• Optimizing sensitivity to minimize false negatives (medical criticality)
• Aggregating patch-level predictions to patient-level diagnosis (pN-staging)

Solutions

• Stain normalization and histopathology-specific augmentations
• Focal loss and weighted cross-entropy for class imbalance handling
• ResNet and EfficientNet architectures with early stopping and checkpointing
• Grad-CAM and attention visualization for medical interpretability

Results

• Complete pipeline: exploration → preprocessing → training → evaluation → interpretability
• Domain shift analysis by hospital source
• Medical metrics: sensitivity, specificity, AUC-ROC
• 6 Jupyter notebooks covering the end-to-end pipeline

Technologies

Python · PyTorch · ResNet · EfficientNet · Grad-CAM · OpenCV · Scikit-learn · Plotly