Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions

TL;DR

This paper reviews recent advances in Multiple Instance Learning techniques for analyzing gigapixel whole slide images in pathology, highlighting challenges, methodologies, and future research directions.

Contribution

It provides a comprehensive overview of MIL applications in WSI analysis, including novel methods like attention mechanisms and graph neural networks, and discusses future opportunities.

Findings

MIL effectively addresses WSI analysis challenges

Attention mechanisms improve model interpretability

Future research directions identified

Abstract

Whole slide images (WSIs) are gigapixel-scale digital images of H\&E-stained tissue samples widely used in pathology. The substantial size and complexity of WSIs pose unique analytical challenges. Multiple Instance Learning (MIL) has emerged as a powerful approach for addressing these challenges, particularly in cancer classification and detection. This survey provides a comprehensive overview of the challenges and methodologies associated with applying MIL to WSI analysis, including attention mechanisms, pseudo-labeling, transformers, pooling functions, and graph neural networks. Additionally, it explores the potential of MIL in discovering cancer cell morphology, constructing interpretable machine learning models, and quantifying cancer grading. By summarizing the current challenges, methodologies, and potential applications of MIL in WSI analysis, this survey aims to inform researchers about the state of the field and inspire future research directions.