CAMIL: channel attention-based multiple instance learning for whole slide image classification.

ABSTRACT:

Motivation

The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. Multiple instance learning (MIL) provides a robust framework for analyzing whole slide images with slide-level labels at gigapixel resolution. However, existing MIL models typically focus on modeling the relationships between instances while neglecting the variability across the channel dimensions of instances, which prevents the model from fully capturing critical information in the channel dimension.

Results

To address this issue, we propose a plug-and-play module called Multi-scale Channel Attention Block (MCAB), which models the interdependencies between channels by leveraging local features with different receptive fields. By alternately stacking four l

SUBMITTER: Mao J 

PROVIDER: S-EPMC11802473 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature