用超图学习捕捉病理图像细节，解决基因组与病理数据不平衡问题。
Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance
- 引入超图学习建模病理图像的上下文与层级关系。
- 在五个TCGA数据集上提升3.4%以上C-Index指标。
- 适合关注多模态癌症生存预测的研究者。
多模态病理-基因组分析在癌症生存预测中日益重要。现有研究多采用多实例学习聚合病理切片级特征，忽略了病理图像中上下文与层级细节的信息损失。此外，病理与基因组数据在粒度和维度上的差异导致显著的模态不平衡，高空间分辨率的病理数据占据主导地位，压制了基因组信息的贡献。本文提出一种多模态生存预测框架，通过超图学习有效捕获病理图像的上下文与层级细节，并采用模态再平衡机制与交互对齐融合策略，动态调整两模态贡献，缓解病理-基因组不平衡。在五个TCGA数据集上进行定量与定性实验，结果表明，该模型在C-Index性能上优于先进方法超过3.4%。
原文摘要 · Abstract (English)
Multimodal pathology-genomic analysis has become increasingly prominent in cancer survival prediction. However, existing studies mainly utilize multi-instance learning to aggregate patch-level features, neglecting the information loss of contextual and hierarchical details within pathology images. Furthermore, the disparity in data granularity and dimensionality between pathology and genomics leads to a significant modality imbalance. The high spatial resolution inherent in pathology data renders it a dominant role while overshadowing genomics in multimodal integration. In this paper, we propose a multimodal survival prediction framework that incorporates hypergraph learning to effectively capture both contextual and hierarchical details from pathology images. Moreover, it employs a modality rebalance mechanism and an interactive alignment fusion strategy to dynamically reweight the contributions of the two modalities, thereby mitigating the pathology-genomics imbalance. Quantitative and qualitative experiments are conducted on five TCGA datasets, demonstrating that our model outperforms advanced methods by over 3.4\% in C-Index performance.
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