ADMIL: Attention-Distilled Multiple Instance Learning for Selective Foundation Model Inference in Pathology

Duncan Stothers; Ren-Chin Wu; William Lotter

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arXiv · 2608.22066

ADMIL: Attention-Distilled Multiple Instance Learning for Selective Foundation Model Inference in Pathology

Duncan Stothers·Ren-Chin Wu·William Lotter

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Abstract

Attention-based multiple instance learning (ABMIL) using pathology foundation model embeddings is effective for slide-level tasks, but exhaustive inference requires applying a large image encoder to every foreground tile despite the subsequent attention distribution often concentrating over a small subset of informative regions. We introduce ADMIL (Attention-Distilled Multiple Instance Learning), a selective-compute framework that distills an ABMIL teacher's attention into a lightweight tile-selection model, PriorNet. Using an EfficientNet architecture, PriorNet learns the teacher attention distribution from raw tile pixels with KL divergence; at inference, it scores the foreground pool, selects the top-K tiles, and invokes the expensive foundation model only on that subset before a selected-bag ABMIL student predicts the slide label. Across BRACS, PANDA, and CAMELYON16, ADMIL matches full-teacher headline performance at K=4, 8, and 128 tiles, respectively, avoiding >98% of foundation model (Virchow2) tile embeddings and model inference FLOPs. Random and teacher-attention oracle controls show that this result depends on task-relevant selection rather than tile-count reduction alone. Quantitative and qualitative analyses suggest that PriorNet recovers the teacher's tile ordering with high fidelity while focusing on task-relevant morphological regions. ADMIL shows that nearly all expensive tile encodings can be removed without sacrificing slide-level performance, providing a potential path for more efficient deployment in clinical settings where latency and compute costs are key considerations.

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Duncan Stothers, Ren-Chin Wu, William Lotter. 2026-08-22. ADMIL: Attention-Distilled Multiple Instance Learning for Selective Foundation Model Inference in Pathology. https://arxiv.org/abs/2608.22066

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