MergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification

TL;DR

This paper introduces MergeUp-augmented semi-weakly supervised learning (SWS-MIL) for WSI classification, combining adaptive pseudo bag augmentation and a novel feature merging technique to improve accuracy on large pathology datasets.

Contribution

It proposes a semi-weakly supervised learning framework with adaptive pseudo bag augmentation and MergeUp feature merging, enhancing WSI classification performance over existing methods.

Findings

Outperforms state-of-the-art methods on CAMELYON-16, BRACS, and TCGA-LUNG datasets.

Demonstrates improved accuracy and robustness in WSI classification.

Validates effectiveness of MergeUp and AdaPse techniques through extensive experiments.

Abstract

Recent advancements in computational pathology and artificial intelligence have significantly improved whole slide image (WSI) classification. However, the gigapixel resolution of WSIs and the scarcity of manual annotations present substantial challenges. Multiple instance learning (MIL) is a promising weakly supervised learning approach for WSI classification. Recently research revealed employing pseudo bag augmentation can encourage models to learn various data, thus bolstering models' performance. While directly inheriting the parents' labels can introduce more noise by mislabeling in training. To address this issue, we translate the WSI classification task from weakly supervised learning to semi-weakly supervised learning, termed SWS-MIL, where adaptive pseudo bag augmentation (AdaPse) is employed to assign labeled and unlabeled data based on a threshold strategy. Using the "student-teacher" pattern, we introduce a feature augmentation technique, MergeUp, which merges bags with low-priority bags to enhance inter-category information, increasing training data diversity. Experimental results on the CAMELYON-16, BRACS, and TCGA-LUNG datasets demonstrate the superiority of our method over existing state-of-the-art approaches, affirming its efficacy in WSI classification.