DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification.

ABSTRACT: Multiple instance learning (MIL) stands as a powerful approach in weakly supervised learning, regularly employed in histological whole slide image (WSI) classification for detecting tumorous lesions. However, existing mainstream MIL methods focus on modeling correlation between instances while overlooking the inherent diversity among instances. However, few MIL methods have aimed at diversity modeling, which empirically show inferior performance but with a high computational cost. To bridge this gap, we propose a novel MIL aggregation method based on diverse global representation (DGR-MIL), by modeling diversity among instances through a set of global vectors that serve as a summary of all instances. First, we turn the instance correlation into the similarity between instance embeddings an

SUBMITTER: Zhu W 

PROVIDER: S-EPMC12425359 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature