GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914002421940224 |
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| author | Shan, Xuhao Ge, Ruiquan Liu, Jikui Wu, Linglong Zhang, Chi Liu, Siqi Qin, Wenjian Min, Wenwen Elazab, Ahmed Wang, Changmiao |
| author_facet | Shan, Xuhao Ge, Ruiquan Liu, Jikui Wu, Linglong Zhang, Chi Liu, Siqi Qin, Wenjian Min, Wenwen Elazab, Ahmed Wang, Changmiao |
| contents | In the field of multimodal medical data analysis, leveraging diverse types of data and understanding their hidden relationships continues to be a research focus. The main challenges lie in effectively modeling the complex interactions between heterogeneous data modalities with distinct characteristics while capturing both local and global dependencies across modalities. To address these challenges, this paper presents a two-stage multimodal prognosis model, GraphMMP, which is based on graph neural networks. The proposed model constructs feature graphs using mutual information and features a global fusion module built on Mamba, which significantly boosts prognosis performance. Empirical results show that GraphMMP surpasses existing methods on datasets related to liver prognosis and the METABRIC study, demonstrating its effectiveness in multimodal medical prognosis tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17478 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis Shan, Xuhao Ge, Ruiquan Liu, Jikui Wu, Linglong Zhang, Chi Liu, Siqi Qin, Wenjian Min, Wenwen Elazab, Ahmed Wang, Changmiao Computer Vision and Pattern Recognition In the field of multimodal medical data analysis, leveraging diverse types of data and understanding their hidden relationships continues to be a research focus. The main challenges lie in effectively modeling the complex interactions between heterogeneous data modalities with distinct characteristics while capturing both local and global dependencies across modalities. To address these challenges, this paper presents a two-stage multimodal prognosis model, GraphMMP, which is based on graph neural networks. The proposed model constructs feature graphs using mutual information and features a global fusion module built on Mamba, which significantly boosts prognosis performance. Empirical results show that GraphMMP surpasses existing methods on datasets related to liver prognosis and the METABRIC study, demonstrating its effectiveness in multimodal medical prognosis tasks. |
| title | GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.17478 |