用互信息建图+Mamba融合，提升多模态医疗预后预测
GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis
- 基于互信息构建异构数据特征图，捕捉模态间复杂关系
- 引入Mamba全局融合模块，在肝病与METABRIC数据集上表现更优
- 适合做多模态医疗预测的研究者参考
在多模态医疗数据分析领域，如何有效利用不同类型的医疗数据并理解其潜在关联仍是研究重点。主要挑战在于建模异质数据模态间的复杂交互关系，同时捕捉跨模态的局部与全局依赖。为此，本文提出一种两阶段多模态预后模型GraphMMP，基于图神经网络构建。该模型通过互信息构造特征图，并设计基于Mamba的全局融合模块，显著提升预后性能。实验结果表明，GraphMMP在肝病预后相关数据集和METABRIC研究数据集上均优于现有方法，验证了其在多模态医疗预后任务中的有效性。
原文摘要 · Abstract (English)
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.
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