---
title: "Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance"
authors:
  - "Mingcheng Qu"
  - "Guang Yang"
  - "Donglin Di"
  - "Tonghua Su"
  - "Yue Gao"
  - "Yang Song"
  - "Lei Fan"
doi: "10.24963/ijcai.2025/201"
url: "https://ijcai.org/proceedings/2025/201"
pdf: "https://ijcai.org/proceedings/2025/0201.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "1802-1810"
track: "Main Track"
keywords:
  - "Computer Vision: CV: Multimodal learning"
  - "Computer Vision: CV: Biomedical image analysis"
---

# 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

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 1802-1810. DOI: [10.24963/ijcai.2025/201](https://doi.org/10.24963/ijcai.2025/201)

## 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. Code: https://github.com/MCPathology/MRePath.