LadderMIL: Multiple-instance Learning with Coarse-to-Fine Self-Distillation.

Authors

Shuyang Wu, Yifu Qiu, Ines P Nearchou, Sandrine Prost, Jonathan A Fallowfield, Hideki Ueno, Hitoshi Tsuda, David J Harrison, Hakan Bilen, Timothy J Kendall

Published in

IEEE journal of biomedical and health informatics. Volume PP. Aug 19, 2026. Epub Aug 19, 2026.

Abstract

Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervision is typically provided only at the bag level, hindering the integrated consideration of instance and bag-level information during the analysis. In this work, we present LadderMIL, a framework designed to improve MIL through two perspectives: (1) employing instance-level supervision and (2) learning inter-instance contextual information at bag level. Firstly, we propose a novel Coarse-to-Fine Self-Distillation (CFSD) paradigm that probes and distils a network trained with bag-level information to adaptively obtain instance-level labels which could effectively provide the instance-level supervision for the same network in a self-improving way. Secondly, to capture inter-instance contextual information in WSI, we propose a Contextual Encoding Generator (CEG), which encodes the contextual appearance of instances within a bag. We also theoretically and empirically prove the instance-level learnability of CFSD. Our LadderMIL is evaluated on multiple clinically relevant benchmarking tasks including breast cancer receptor status classification, multi-class subtype classification, tumour classification, and prognosis prediction. By comparing with the best baseline, average improvements of 6.4%, 5.4% and 0.8% in AUC, F1-score, and concordance index (C-index) are demonstrated across the five benchmarks; while average improvements of 7.6% and 10.4% in AUC and F1-score are shown in the external validation cohort.

PMID:
42616626
Bibliographic data and abstract were imported from PubMed on 20 Aug 2026.

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