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}.

翻译：生存预测是一项复杂的序数回归任务，旨在预测患者队列中的生存系数排名，通常通过分析患者的全切片图像来实现。现有的深度学习方法主要采用弱监督下的多实例学习或图神经网络。然而，大多数方法无法揭示不同尺度下多种生物实体类型（例如细胞簇和组织块）之间的多样化交互关系，而这种交互对于患者生存预测至关重要。鉴于此，我们提出了一种新颖的多尺度异质性感知超图表示框架。具体而言，该框架首先构建一个多尺度异质性感知超图，并为每个节点分配其生物实体类型；随后，它挖掘图结构上节点之间的多样化交互关系，以获取全局表示。实验结果表明，我们的方法在三个基准数据集上均优于现有最先进方法。代码已公开于 \href{https://github.com/Hanminghao/H2GT}{https://github.com/Hanminghao/H2GT}。