Enabling Progressive Whole-slide Image Analysis with Multi-scale Pyramidal Network
This work addresses computational pathology challenges by providing a plug-and-play module for more efficient and flexible multi-scale analysis, though it is incremental as it builds upon existing attention-based methods.
The paper tackles the problem of inefficient and inflexible multi-scale feature analysis in whole-slide image computational pathology by proposing the Multi-scale Pyramidal Network (MSPN), which consistently improves multiple-instance learning frameworks across various tasks and configurations.
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.