Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images

TL;DR

This paper introduces a multi-scale heterogeneity-aware hypergraph framework for analyzing whole slide images in histopathology, improving survival prediction by capturing diverse biological interactions across scales.

Contribution

It proposes a novel hypergraph representation that models multi-scale biological heterogeneity and interactions, advancing survival prediction accuracy in histopathology images.

Findings

Outperforms state-of-the-art methods on three benchmark datasets.

Effectively captures diverse multi-scale biological interactions.

Provides a publicly available code implementation.

Abstract

Survival prediction is a complex ordinal regression task that aims to predict the survival coefficient ranking among a cohort of patients, typically achieved by analyzing patients' whole slide images. Existing deep learning approaches mainly adopt multiple instance learning or graph neural networks under weak supervision. Most of them are unable to uncover the diverse interactions between different types of biological entities(\textit{e.g.}, cell cluster and tissue block) across multiple scales, while such interactions are crucial for patient survival prediction. In light of this, we propose a novel multi-scale heterogeneity-aware hypergraph representation framework. Specifically, our framework first constructs a multi-scale heterogeneity-aware hypergraph and assigns each node with its biological entity type. It then mines diverse interactions between nodes on the graph structure to obtain a global representation. Experimental results demonstrate that our method outperforms state-of-the-art approaches on three benchmark datasets. Code is publicly available at \href{https://github.com/Hanminghao/H2GT}{https://github.com/Hanminghao/H2GT}.