Han Xiangmin, Zhou Huijian, Tian Zhiqiang, Du Shaoyi, Gao Yue
IEEE Trans Pattern Anal Mach Intell. 2025 Jul;47(7):6006-6021. doi: 10.1109/TPAMI.2025.3557391.
Survival prediction on histopathology whole slide images (WSIs) involves the analysis of multi-level complex correlations, such as inter-correlations among patients and intra-correlations within gigapixel histopathology images. However, the current graph-based methods for WSI analysis mainly focus on the exploration of pairwise correlations, resulting in the loss of high-order correlations. Hypergraph-based methods can handle such high-order correlations, while existing hypergraph-based methods fail to integrate multi-level high-order correlations into a unified framework, which limits the representation capability of WSIs. In this work, we propose an inter-intra hypergraph computation (I$^{2}$2HGC) framework to address this issue. The I$^{2}$2HGC framework implements multi-level hypergraph computation for survival prediction on WSIs, namely intra-hypergraph computation and inter-hypergraph computation. Specifically, the intra-hypergraph computation considers each patch sampled from the histopathology WSI as a vertex of the intra-hypergraph and models the high-order correlations among all patches of an individual WSI in both topology and semantic feature spaces using a hypergraph structure. Then, the intra-hypergraph module generates the intra-embedding and intra-risk for each patient. Subsequently, the inter-hypergraph computation employs these intra-embeddings as features for each patient to form the population-level high-order correlations using data- and knowledge-driven hypergraph modeling strategies. Finally, the intra-risks and the inter-risks are fused for the final survival prediction of each patient. Extensive experimental results on four widely used TCGA carcinoma datasets are presented. We demonstrate that the hypergraph structure captures significantly richer correlations than the graph structure, encompassing all pairwise correlations as well as higher-order interactions through hyperedges. For WSIs with a vast number of pixels and complex correlations, hypergraph-based methods effectively capture topological and semantic information while mitigating the exponential growth of pairwise edges, offering practical advantages for large-scale medical image analysis.
基于组织病理学全切片图像（WSIs）的生存预测涉及多层次复杂相关性分析，例如患者之间的相互关系以及千兆像素组织病理学图像内部的相关性。然而，当前基于图的WSI分析方法主要侧重于探索成对相关性，导致高阶相关性丢失。基于超图的方法可以处理此类高阶相关性，但现有的基于超图的方法未能将多层次高阶相关性集成到统一框架中，这限制了WSIs的表示能力。在这项工作中，我们提出了一种内部-外部超图计算（I²2HGC）框架来解决这个问题。I²2HGC框架对WSIs的生存预测实现了多层次超图计算，即内部超图计算和外部超图计算。具体而言，内部超图计算将从组织病理学WSI中采样的每个补丁视为内部超图的一个顶点，并使用超图结构在拓扑和语义特征空间中对单个WSI的所有补丁之间的高阶相关性进行建模。然后，内部超图模块为每个患者生成内部嵌入和内部风险。随后，外部超图计算将这些内部嵌入作为每个患者的特征，使用数据驱动和知识驱动的超图建模策略来形成群体水平的高阶相关性。最后，将内部风险和外部风险融合以进行每个患者的最终生存预测。我们在四个广泛使用的TCGA癌症数据集上展示了大量实验结果。我们证明，超图结构比图结构捕获的相关性要丰富得多，通过超边包含了所有成对相关性以及高阶相互作用。对于具有大量像素和复杂相关性的WSIs，基于超图的方法有效地捕获拓扑和语义信息，同时减轻成对边的指数增长，为大规模医学图像分析提供了实际优势。
