Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhuang, Farica Yang, Shu Aliyeva, Dinara Wen, Zixuan Duong-Tran, Duy Davatzikos, Christos Chen, Tianlong Wang, Song Shen, Li |
| author_facet | Zhuang, Farica Yang, Shu Aliyeva, Dinara Wen, Zixuan Duong-Tran, Duy Davatzikos, Christos Chen, Tianlong Wang, Song Shen, Li |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts Zhuang, Farica Yang, Shu Aliyeva, Dinara Wen, Zixuan Duong-Tran, Duy Davatzikos, Christos Chen, Tianlong Wang, Song Shen, Li Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2512.10966 |