Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance
Journal:
arXiv
Published Date:
May 17, 2025
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.
