{"ID":2878987,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17478","arxiv_id":"2508.17478","title":"GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis","abstract":"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.","short_abstract":"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 b...","url_abs":"https://arxiv.org/abs/2508.17478","url_pdf":"https://arxiv.org/pdf/2508.17478v1","authors":"[\"Xuhao Shan\",\"Ruiquan Ge\",\"Jikui Liu\",\"Linglong Wu\",\"Chi Zhang\",\"Siqi Liu\",\"Wenjian Qin\",\"Wenwen Min\",\"Ahmed Elazab\",\"Changmiao Wang\"]","published":"2025-08-24T18:06:20Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Graph Neural Network\"]","has_code":false}