{"type":"ScholarlyArticle","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","authors":[{"name":"Mingcheng Qu","slug":"mingcheng-qu","given":"Mingcheng","family":"Qu","affiliation":""},{"name":"Guang Yang","slug":"guang-yang","given":"Guang","family":"Yang","affiliation":""},{"name":"Donglin Di","slug":"donglin-di","given":"Donglin","family":"Di","affiliation":""},{"name":"Tonghua Su","slug":"tonghua-su","given":"Tonghua","family":"Su","affiliation":""},{"name":"Yue Gao","slug":"yue-gao","given":"Yue","family":"Gao","affiliation":""},{"name":"Yang Song","slug":"yang-song","given":"Yang","family":"Song","affiliation":""},{"name":"Lei Fan","slug":"lei-fan","given":"Lei","family":"Fan","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/201","url":"https://ijcai.org/proceedings/2025/201","pdf":"https://ijcai.org/proceedings/2025/0201.pdf","markdown":"https://ijcai.org/proceedings/2025/201.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/201","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":201,"pages":{"first":1802,"last":1810},"keywords":["Computer Vision: CV: Multimodal learning","Computer Vision: CV: Biomedical image analysis"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}