#multiple-instance-learning News & Analysis

Aligning Cellular Sheaves with Classifier Attention for Interpretable Weakly-Supervised Pathology Localization

Researchers propose a novel approach combining cellular sheaves with attention-based multiple instance learning to improve interpretability in weakly-supervised pathology image classification. The method achieves 0.940 patch-level AUC on Camelyon16 and successfully aligns attention maps with diagnostic regions, addressing a critical gap where models classify correctly without focusing on actual lesions.