{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:33:36Z","timestamp":1774917216093,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/201","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"1802-1810","source":"Crossref","is-referenced-by-count":3,"title":["Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance"],"prefix":"10.24963","author":[{"given":"Mingcheng","family":"Qu","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology"}]},{"given":"Guang","family":"Yang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}]},{"given":"Donglin","family":"Di","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Tonghua","family":"Su","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}]},{"given":"Yue","family":"Gao","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Yang","family":"Song","sequence":"additional","affiliation":[{"name":"University of New South Wales"}]},{"given":"Lei","family":"Fan","sequence":"additional","affiliation":[{"name":"University of New South Wales"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:16Z","timestamp":1758627196000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/201"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/201","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}