通过多激活平面动态构建图结构，提升多模态医疗诊断准确率
MAPI-GNN: Multi-Activation Plane Interaction Graph Neural Network for Multimodal Medical Diagnosis
- 从语义解耦的特征子空间学习多维图模式
- 在1300+患者数据上超越现有方法性能
- 适合需要精准建模病患特异性关系的医疗场景
图神经网络因其内在的关系建模能力，在多模态医疗诊断中日益受到关注。然而，其效果常受限于依赖单一静态图结构的现状，该结构基于无差别特征构建，难以捕捉患者特异性的病理关系。为此，本文提出多激活平面交互图神经网络（MAPI-GNN），突破单图范式，从语义解耦的特征子空间中学习多维度图谱表征。框架首先通过多维判别器挖掘潜在的图感知模式；这些模式随后指导动态构建一组激活图；最终，多维表征由关系融合引擎聚合并上下文化，实现稳健诊断。在包含超过1300名患者的两项多样化任务上进行的大量实验表明，MAPI-GNN显著优于当前最优方法。
原文摘要 · Abstract (English)
Graph neural networks are increasingly applied to multimodal medical diagnosis for their inherent relational modeling capabilities. However, their efficacy is often compromised by the prevailing reliance on a single, static graph built from indiscriminate features, hindering the ability to model patient-specific pathological relationships. To this end, the proposed Multi-Activation Plane Interaction Graph Neural Network (MAPI-GNN) reconstructs this single-graph paradigm by learning a multifaceted graph profile from semantically disentangled feature subspaces. The framework first uncovers latent graph-aware patterns via a multi-dimensional discriminator; these patterns then guide the dynamic construction of a stack of activation graphs; and this multifaceted profile is finally aggregated and contextualized by a relational fusion engine for a robust diagnosis. Extensive experiments on two diverse tasks, comprising over 1300 patient samples, demonstrate that MAPI-GNN significantly outperforms state-of-the-art methods.
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