Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning

TL;DR

This paper introduces a novel approach to enhance Multiple Instance Learning for digital pathology by incorporating pseudo-label correction, significantly improving bag- and instance-level classification accuracy on public datasets.

Contribution

It proposes a denoising mutual knowledge distillation framework that bridges MIL and fully supervised learning, reducing noisy labels and boosting performance.

Findings

Improved bag-level classification accuracy

Enhanced instance-level prediction precision

Effective on multiple public pathology datasets

Abstract

Multiple Instance Learning is the predominant method for Whole Slide Image classification in digital pathology, enabling the use of slide-level labels to supervise model training. Although MIL eliminates the tedious fine-grained annotation process for supervised learning, whether it can learn accurate bag- and instance-level classifiers remains a question. To address the issue, instance-level classifiers and instance masks were incorporated to ground the prediction on supporting patches. These methods, while practically improving the performance of MIL methods, may potentially introduce noisy labels. We propose to bridge the gap between commonly used MIL and fully supervised learning by augmenting both the bag- and instance-level learning processes with pseudo-label correction capabilities elicited from weak to strong generalization techniques. The proposed algorithm improves the performance of dual-level MIL algorithms on both bag- and instance-level predictions. Experiments on public pathology datasets showcase the advantage of the proposed methods.