用专家融合模型提升阿尔茨海默病早期诊断的准确性与可解释性
Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
- 将脑区视为独立专家，通过门控网络自适应融合多模态影像数据
- 在ADNI数据集上达到主流模型水平的诊断准确率
- 提供每个患者个体化融合权重，揭示不同脑区贡献差异
阿尔茨海默病（AD）的精准早期诊断对干预至关重要，需整合多模态神经影像互补信息。然而传统融合方法常采用特征简单拼接，难以自适应地平衡淀粉样蛋白PET和MRI等生物标志物在不同脑区的贡献。本文提出MREF-AD——一种用于AD诊断的多模态区域专家融合模型。该模型基于混合专家（MoE）框架，将每种模态内的中尺度脑区视为独立专家，并通过门控网络学习受试者特定的融合权重。利用阿尔茨海默病神经影像计划（ADNI）中的表格化神经影像与人口统计学信息，MREF-AD在性能上超越多种经典与深度基线模型，同时提供结构与分子影像联合诊断的模态级与区域级可解释性。代码已开源：https://github.com/PennShenLab/mref-ad。
原文摘要 · Abstract (English)
Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data. However, conventional fusion approaches often rely on simple concatenation of features, which cannot adaptively balance the contributions of biomarkers such as amyloid PET and MRI across brain regions. In this work, we propose MREF-AD, a Multimodal Regional Expert Fusion model for AD diagnosis. It is a Mixture-of-Experts (MoE) framework that models mesoscopic brain regions within each modality as independent experts and employs a gating network to learn subject-specific fusion weights. Utilizing tabular neuroimaging and demographic information from the Alzheimer's Disease Neuroimaging Initiative (ADNI), MREF-AD achieves competitive performance over strong classic and deep baselines while providing interpretable, modality- and region-level insight into how structural and molecular imaging jointly contribute to AD diagnosis. The source code is available at https://github.com/PennShenLab/mref-ad.
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