GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology

GRAPHITE addresses a critical gap in medical AI adoption: The framework tackles the "black-box" problem that has limited clinical acceptance of deep learning models in cancer diagnosis by providing explainable visualizations that align with how pathologists actually reason through diagnoses.

The model demonstrates strong clinical performance metrics: GRAPHITE achieved an AUROC of 0.94 and maintained high performance across different decision thresholds (threshold robustness of 0.70), suggesting it could reliably support pathologists' decision-making in real clinical settings.

Multi-scale analysis mimics how pathologists work: By extracting tissue features at various magnification levels and using graph attention networks, GRAPHITE captures the hierarchical way pathologists examine tissue samples—from broad patterns down to cellular details—making its reasoning more transparent and clinically intuitive.

Interpretability outperformed traditional XAI methods: GRAPHITE achieved superior performance compared to conventional explainability approaches (mAP of 0.56), suggesting that specialized methods designed for medical imaging can provide clearer, more actionable explanations than generic interpretability techniques.

Clinical validation remains limited but promising: While tested on a relatively small dataset (53 pathologist-annotated samples), the framework's high clinical utility score (AUDC of 4.17e+5) indicates potential for supporting pathologists across different diagnostic thresholds, though larger-scale clinical validation would be needed before widespread adoption.