AI RESEARCH
PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning
arXiv CS.CV
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ArXi:2604.12100v1 Announce Type: new Whole-slide image (WSI) classification in computational pathology is commonly formulated as slide-level Multiple Instance Learning (MIL) with a single global bag representation. However, slide-level MIL is fundamentally underconstrained: optimizing only global labels encourages models to aggregate features without learning anatomically meaningful localization. This creates a mismatch between the scale of supervision and the scale of clinical reasoning.