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}{this https URL}.

通过构建多尺度异质感知超图，挖掘生物实体间的多样互动以获得全局表示，并在三个基准数据集上表现出优越性能。