动态融合脑功能与结构信息，提升创伤后癫痫早期诊断准确率
DynFS-MoE: Dynamic Functional-Structural Mixture-of-Experts for Post-Traumatic Epilepsy Diagnosis
- 通过时序编码和条件路由动态融合功能与结构连接数据
- 在三个分类任务中均优于静态融合方法，实现更高诊断精度
- 可解释性强，识别出关键脑区交互模式，适合临床风险评估
创伤后癫痫（PTE）是创伤性脑损伤（TBI）的严重并发症，但因其引发的复杂脑结构与功能改变，早期识别仍具挑战。本文提出一种动态多模态混合专家（MoE）框架，通过时序感知的功能-结构编码与类别条件专家路由，融合功能与结构连接信息。模态特异性及跨模态专家学习互补表征，而模态-类别MoE（MCoE）模块根据分类目标动态调整专家权重。在三个二分类任务中的实验结果表明，该框架持续优于静态融合基线；高可解释性分析进一步揭示了有意义的脑区兴趣点（ROIs）交互模式。该动态多模态专家框架有效捕捉了依赖类别的脑网络交互模式，为PTE诊断与风险分层提供了可解释的方法。
原文摘要 · Abstract (English)
Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Yet, early identification remains challenging due to the complex structural and functional alterations it induces in the brain. To address this, we propose a dynamic multimodal Mixture-of-Experts (MoE) framework that integrates functional and structural connectivity through time-aware functional-structural encoding and class-conditioned expert routing. Within this framework, modality-specific and cross-modal experts learn complementary representations, while a Modality-Class MoE (MCoE) module dynamically adjusts expert weights according to each classification objective. Experimental results across three binary classification tasks demonstrate that the framework consistently outperforms static fusion baselines, and high-interpretability analyses further reveal meaningful regions of interest (ROIs) interactions. This dynamic multimodal expert framework effectively captures class-dependent brain interaction patterns and provides an interpretable approach for PTE diagnosis and risk stratification.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。