Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology 事件

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology arXiv:2606.06224v1 Announce Type: new Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primarily rely on heatmaps that highlight influential regions but do not explain how evidence from different tissue regions is combined to produce a prediction. This limits interpretability, especially when decisions depe