Shuyang Wu

Shuyang Wu, Yifu Qiu, Ines P. Nearchou et al.

Multiple-instance Learning (MIL) is commonly used to undertake computational pathology (CPath) tasks, and the use of multi-scale patches allows diverse features across scales to be learned. Previous studies using multi-scale features in clinical applications rely on multiple inputs across magnifications with late feature fusion, which does not retain the link between features across scales while the inputs are dependent on arbitrary, manufacturer-defined magnifications, being inflexible and computationally expensive. In this paper, we propose the Multi-scale Pyramidal Network (MSPN), which is plug-and-play over attention-based MIL that introduces progressive multi-scale analysis on WSI. Our MSPN consists of (1) grid-based remapping that uses high magnification features to derive coarse features and (2) the coarse guidance network (CGN) that learns coarse contexts. We benchmark MSPN as an add-on module to 4 attention-based frameworks using 4 clinically relevant tasks across 3 types of foundation model, as well as the pre-trained MIL framework. We show that MSPN consistently improves MIL across the compared configurations and tasks, while being lightweight and easy-to-use.

Shuyang Wu, Yifu Qiu, Ines P. Nearchou et al.

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. Average improvements of 8.1%, 11% and 2.4% in AUC, F1-score, and C-index, respectively, are demonstrated across the five benchmarks, compared to the best baseline.