A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement
Abstract Cancer survival prediction, as a critical task in clinical prognosis analysis, holds significant importance for guiding treatment decisions and patient management. This paper proposes a survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement (MSHG-...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00202-3 |
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| _version_ | 1849225865422962688 |
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| author | Chaofeng Yang Yongjie Liang Fan Qin Yulong Cao Peiyuan Wang Jiaying Fan Bizhong Wei |
| author_facet | Chaofeng Yang Yongjie Liang Fan Qin Yulong Cao Peiyuan Wang Jiaying Fan Bizhong Wei |
| author_sort | Chaofeng Yang |
| collection | DOAJ |
| description | Abstract Cancer survival prediction, as a critical task in clinical prognosis analysis, holds significant importance for guiding treatment decisions and patient management. This paper proposes a survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement (MSHG-CMR), which achieves deep joint modeling and efficient information fusion of pathological images and multi-omics data by integrating multi-scale hypergraph enhancement and cross-modal refinement Transformer technology. In the method design, the hypergraph construction module captures high-order feature associations of pathological images at different resolutions and effectively aligns feature distributions across scales through homomorphic nonlinear mapping; subsequently, the cross-modal refinement Transformer module based on rotary encoding achieves dynamic information transfer and complementary refinement between visual features and multi-omics data through attention mechanisms and position decay designs; finally, a feature fusion attention mechanism is introduced to fuse multi-modal features, which both suppresses inter-modal redundancy and effectively mitigates information conflicts, thereby enhancing the precision of individualized prognosis analysis. Experimental results show that MSHG-CMR improves the C-index performance by 1.7% overall compared to existing SOTA methods on the TCGA-BLCA dataset and by 1.1% on the TCGA-LUAD dataset, demonstrating its generalizability across different cancer types. Code is available at: https://github.com/MUYI-XIAN/MSHG-CMR . |
| format | Article |
| id | doaj-art-bc2bff0fcc9e4e7e83575ad6e31413be |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-bc2bff0fcc9e4e7e83575ad6e31413be2025-08-24T11:53:38ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137711810.1007/s44443-025-00202-3A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinementChaofeng Yang0Yongjie Liang1Fan Qin2Yulong Cao3Peiyuan Wang4Jiaying Fan5Bizhong Wei6School of Computer Science and Information Security, Guilin University of Electronic TechnologyLife Science Institute, Guangxi Medical UniversitySchool of Computer Science and Information Security, Guilin University of Electronic TechnologySchool of Computer Science and Information Security, Guilin University of Electronic TechnologySchool of Computer Science and Information Security, Guilin University of Electronic TechnologySchool of Computer Science and Information Security, Guilin University of Electronic TechnologySchool of Computer Science and Information Security, Guilin University of Electronic TechnologyAbstract Cancer survival prediction, as a critical task in clinical prognosis analysis, holds significant importance for guiding treatment decisions and patient management. This paper proposes a survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement (MSHG-CMR), which achieves deep joint modeling and efficient information fusion of pathological images and multi-omics data by integrating multi-scale hypergraph enhancement and cross-modal refinement Transformer technology. In the method design, the hypergraph construction module captures high-order feature associations of pathological images at different resolutions and effectively aligns feature distributions across scales through homomorphic nonlinear mapping; subsequently, the cross-modal refinement Transformer module based on rotary encoding achieves dynamic information transfer and complementary refinement between visual features and multi-omics data through attention mechanisms and position decay designs; finally, a feature fusion attention mechanism is introduced to fuse multi-modal features, which both suppresses inter-modal redundancy and effectively mitigates information conflicts, thereby enhancing the precision of individualized prognosis analysis. Experimental results show that MSHG-CMR improves the C-index performance by 1.7% overall compared to existing SOTA methods on the TCGA-BLCA dataset and by 1.1% on the TCGA-LUAD dataset, demonstrating its generalizability across different cancer types. Code is available at: https://github.com/MUYI-XIAN/MSHG-CMR .https://doi.org/10.1007/s44443-025-00202-3Multi-modal learningHypergraph enhancementSurvival prediction |
| spellingShingle | Chaofeng Yang Yongjie Liang Fan Qin Yulong Cao Peiyuan Wang Jiaying Fan Bizhong Wei A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement Journal of King Saud University: Computer and Information Sciences Multi-modal learning Hypergraph enhancement Survival prediction |
| title | A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement |
| title_full | A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement |
| title_fullStr | A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement |
| title_full_unstemmed | A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement |
| title_short | A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement |
| title_sort | survival prediction network based on multi scale hypergraph enhancement and cross modal refinement |
| topic | Multi-modal learning Hypergraph enhancement Survival prediction |
| url | https://doi.org/10.1007/s44443-025-00202-3 |
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