Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging

TL;DR

This paper introduces novel methods to enhance continual learning in multiple instance learning models for whole slide imaging, significantly improving adaptability and efficiency in medical diagnostics.

Contribution

It proposes Attention Knowledge Distillation and Pseudo-Bag Memory Pool to reduce forgetting and memory use in CL-MIL models, advancing the field.

Findings

Improved accuracy on diverse WSI datasets.

Enhanced memory efficiency over existing methods.

Reduced catastrophic forgetting in attention layers.

Abstract

Advances in medical imaging and deep learning have propelled progress in whole slide image (WSI) analysis, with multiple instance learning (MIL) showing promise for efficient and accurate diagnostics. However, conventional MIL models often lack adaptability to evolving datasets, as they rely on static training that cannot incorporate new information without extensive retraining. Applying continual learning (CL) to MIL models is a possible solution, but often sees limited improvements. In this paper, we analyze CL in the context of attention MIL models and find that the model forgetting is mainly concentrated in the attention layers of the MIL model. Using the results of this analysis we propose two components for improving CL on MIL: Attention Knowledge Distillation (AKD) and the Pseudo-Bag Memory Pool (PMP). AKD mitigates catastrophic forgetting by focusing on retaining attention layer knowledge between learning sessions, while PMP reduces the memory footprint by selectively storing only the most informative patches, or ``pseudo-bags'' from WSIs. Experimental evaluations demonstrate that our method significantly improves both accuracy and memory efficiency on diverse WSI datasets, outperforming current state-of-the-art CL methods. This work provides a foundation for CL in large-scale, weakly annotated clinical datasets, paving the way for more adaptable and resilient diagnostic models.