Are Compact Rationales Free? Measuring Tile Selection Headroom in Frozen WSI-MIL
Whole-slide image (WSI) multiple instance learning (MIL) classifiers can achieve strong slide-level AUC while leaving the full-bag prediction opaque. Attention scores are widely reused as post-hoc explanations, but high attention can reflect aggregation preference rather than a compact, model-sufficient rationale. We study post-hoc rationale highlighting for frozen WSI-MIL: given a trained classifier, can its slide-level prediction be recovered from a compact, output-consistent tile subset without retraining the backbone? We instantiate this with Finding Optimal Contextual Instances (FOCI), a lightweight rationale-readout layer over a frozen MIL backbone. FOCI is trained with model-output...
| Authors | Hyun Do Jung, Jungwon Choi, Soojung Choi et al. |
|---|---|
| Primary category | eess.IV |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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