Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
For clinicians diagnosing Alzheimer's disease, this model offers interpretable fusion of amyloid PET and MRI, but the performance gain over baselines is not quantified as SOTA.
MREF-AD uses a Mixture-of-Experts framework to fuse multimodal neuroimaging data for Alzheimer's diagnosis, achieving competitive performance on ADNI data while providing interpretable region- and modality-level insights.
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.