MREF-AD applies a mixture-of-experts architecture to regional brain imaging data from multiple modalities to achieve competitive Alzheimer's diagnosis accuracy on ADNI while supplying region- and modality-level interpretability.
Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
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abstract
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.
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UNVERDICTED 2representative citing papers
IMA-MoE combines multimodal neuroimaging, behavioral, hormonal, and demographic data via token-based mixture-of-experts to outperform baselines at distinguishing binge eating disorder from controls while highlighting sex-specific contributions.
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Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
MREF-AD applies a mixture-of-experts architecture to regional brain imaging data from multiple modalities to achieve competitive Alzheimer's diagnosis accuracy on ADNI while supplying region- and modality-level interpretability.
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IMA-MoE: An Interpretable Modality-Aware Mixture-of-Experts Framework for Characterizing the Neurobiological Signatures of Binge Eating Disorder
IMA-MoE combines multimodal neuroimaging, behavioral, hormonal, and demographic data via token-based mixture-of-experts to outperform baselines at distinguishing binge eating disorder from controls while highlighting sex-specific contributions.