arXiv Machine Learning By Chen Shu, Chengyang Zhang, Boyu Fu, Ting Yin, Wenchuan Zhang, Jie Chen, Yuhao Yi, Hong Bu

ProtoPur: Prototype-Purity Pseudo-Labeling for Weakly Supervised Instance Learning in Pathology

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arXiv Computer Vision
Sep 28

LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation

arXiv:2502.02707v5 Announce Type: replace Abstract: Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervis...

By Shuyang Wu, Yifu Qiu, Ines P. Nearchou, Sandrine Prost, Jonathan A. Fallowfield, Hideki Ueno, Hitoshi Tsuda, David J. Harrison, Hakan Bilen, Timothy J. Kendall
arXiv AI
Jun 2

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

arXiv:2606. 00092v1 Announce Type: cross Abstract: 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.

By Devansh Lalwani, Swapnil Bhat, Maulik Shah