構造化されたプロトタイプベースのマルチインスタンス学習による異種組織病理の一般化

Accurate and generalizable cancer diagnosis from whole slide images (WSIs) remains challenging due to limited fine-grained annotations, complex tumor architectures, and domain shifts across scanners and institutions¹. We introduce StructMIL, a structure-aware and prototype-driven multiple instance learning framework designed for robust and interpretable cancer detection and grading². StructMIL integrates graph-based topological priors with histological context, employs prototype-enhanced pooling for stable and transparent predictions, and incorporates a unified domain-generalization strategy that combines contrastive alignment, adversarial confusion, and consistency regularization. Evaluated on Camelyon16 for breast cancer metastasis detection and PANDA for prostate cancer Gleason grading, StructMIL achieves state-of-the-art performance. On Camelyon16, StructMIL improves cross-center AUC by +3.2% over standard MIL baselines, reaching an AUC of 0.967. On PANDA, it improves cross-scanner Gleason grading robustness with a +7.4% Cohen's Kappa gain compared with prior MIL models, demonstrating substantially reduced performance degradation under domain shift. StructMIL further provides interpretable prototype-based attribution maps that highlight biologically meaningful structures more reliably than conventional MIL and graph-free approaches³. By jointly improving accuracy, interpretability, and generalization across scanners and medical centers, StructMIL offers a practical and clinically aligned solution for large-scale deployment in multi-center computational pathology workflows⁴.