Aligning Cellular Sheaves with Classifier Attention for Interpretable Weakly-Supervised Pathology Localization
Weakly-supervised classification of whole-slide images with attention-based multiple instance learning (ABMIL) on top of foundation features now reaches near-saturation on Camelyon16 slide-level performance, but the corresponding attention maps are an imperfect localization signal: in clinical interpretation, a model that classifies correctly without firing on the actual lesion is hard to trust. We address this gap with cellular sheaves, which equip each vertex and edge of a graph with a finite-dimensional vector space and consistent linear maps between them, providing a principled way to detect local disagreement on graph-structured data. We apply cellular sheaves to weakly-supervised tu...
| Authors | Devansh Lalwani, Swapnil Bhat, Maulik Shah |
|---|---|
| Primary category | cs.CV |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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