ReaMIL: Reasoning- and Evidence-Aware Multiple Instance Learning for Whole-Slide Histopathology

📰 ArXiv cs.AI

ReaMIL is a multiple instance learning approach for whole-slide histopathology that uses a light selection head to identify relevant tiles

advanced Published 8 Apr 2026

Action Steps

Implement a strong MIL backbone for whole-slide histopathology
Add a light selection head to produce soft per-tile gates
Train the model with a budgeted-sufficiency objective using hinge loss
Evaluate the model's performance under a sparsity budget on the number of selected tiles

Who Needs to Know This

This research benefits AI engineers and ML researchers working on medical imaging analysis, as it improves the accuracy of whole-slide histopathology diagnosis

Key Insight

💡 ReaMIL's light selection head and budgeted-sufficiency objective improve the accuracy of whole-slide histopathology diagnosis

Key Takeaways

ReaMIL is a multiple instance learning approach for whole-slide histopathology that uses a light selection head to identify relevant tiles

Full Article

Title: ReaMIL: Reasoning- and Evidence-Aware Multiple Instance Learning for Whole-Slide Histopathology

Abstract:
arXiv:2601.10073v2 Announce Type: replace-cross Abstract: We introduce ReaMIL (Reasoning- and Evidence-Aware MIL), a multiple instance learning approach for whole-slide histopathology that adds a light selection head to a strong MIL backbone. The head produces soft per-tile gates and is trained with a budgeted-sufficiency objective: a hinge loss that enforces the true-class probability to be $\geq \tau$ using only the kept evidence, under a sparsity budget on the number of selected tiles. The bu

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