Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification

TL;DR

This paper introduces C^2Aug, a contrastive cross-bag augmentation method for MIL-based WSI classification that enhances pseudo-bag diversity and feature discrimination, leading to improved model performance.

Contribution

The paper proposes a novel augmentation technique combined with contrastive learning to increase diversity and discrimination in MIL-based WSI classification.

Findings

C^2Aug outperforms state-of-the-art methods across multiple metrics.

Enhanced feature discrimination improves model robustness.

Increased pseudo-bag diversity benefits classification accuracy.

Abstract

Recent pseudo-bag augmentation methods for Multiple Instance Learning (MIL)-based Whole Slide Image (WSI) classification sample instances from a limited number of bags, resulting in constrained diversity. To address this issue, we propose Contrastive Cross-Bag Augmentation ($C^2Aug$) to sample instances from all bags with the same class to increase the diversity of pseudo-bags. However, introducing new instances into the pseudo-bag increases the number of critical instances (e.g., tumor instances). This increase results in a reduced occurrence of pseudo-bags containing few critical instances, thereby limiting model performance, particularly on test slides with small tumor areas. To address this, we introduce a bag-level and group-level contrastive learning framework to enhance the discrimination of features with distinct semantic meanings, thereby improving model performance. Experimental results demonstrate that $C^2Aug$ consistently outperforms state-of-the-art approaches across multiple evaluation metrics.