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arXiv · 2505.11997

Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance

Mingcheng Qu·Guang Yang·Donglin Di·Tonghua Su·Yue Gao·Yang Song·Lei Fan
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Abstract

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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Mingcheng Qu, Guang Yang, Donglin Di, Tonghua Su, Yue Gao, Yang Song, Lei Fan. 2025-05-17. Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance. https://arxiv.org/abs/2505.11997

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