用MRI预测阿尔茨海默病进展，同时识别稳定与恶化患者
Decoding the Alzheimer's Continuum: Interpretable Multi-Gate Routing for Diagnosis and Transition Prediction
- 多门路由专家架构动态捕捉不同阶段脑部变化特征
- 诊断准确率达95.13%，转换预测准确率94.87%
- 可解释性强，揭示个体进展风险的机制依据
阿尔茨海默病（AD）从正常认知（NC）经轻度认知障碍（MCI）发展至痴呆是一个连续过程。然而，多数深度学习方法将其简化为孤立的分类任务，忽视了动态阶段转变。为此，我们提出M$^3$AD框架，仅使用T1加权sMRI，统一解决三分类诊断与阶段转换预测问题。该框架采用可解释的多门混合专家结构，通过专用路由机制动态捕捉诊断特异性病理模式及跨阶段共享结构特征，并融合临床先验（年龄、性别、头围）以提升泛化能力。M$^3$AD在相同实验条件下达到95.13%诊断准确率（优于原有MCLNC的90.44%），转换预测准确率为94.87%。关键的是，多门路由分析揭示了稳定型与进展型MCI的显著专家激活差异，为个体水平进展风险分层提供机制基础。代码已开源。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) manifests as a continuous progression from normal cognition (NC) through mild cognitive impairment (MCI) to dementia. However, most deep learning approaches reduce this continuum to disjointed classification tasks, largely ignoring dynamic stage transitions. To decode this complex progression, we propose M$^3$AD, a unified framework that jointly addresses three-class diagnosis classification and diagnosis stage transition prediction using only T1-weighted sMRI. M$^3$AD leverages an interpretable multi-gate mixture of experts architecture, employing specialized routing mechanisms to dynamically capture both diagnosis-specific pathological patterns and shared structural features across the continuum. It further integrates clinical priors (age, sex, eTIV) via adaptive attention fusion to enhance generalization. M$^3$AD achieves 95.13% accuracy, compared to 90.44% reported by MCLNC under its original experimental setting, and 94.87% for transition prediction. Crucially, analyzing the multi-gate routing reveals distinct expert activation signatures distinguishing stable from progressive MCI, providing a mechanistic basis for individual-level progression risk stratification. Code is available at https://github.com/csyfjiang/M3AD.
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