{"ID":2887371,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01819","arxiv_id":"2508.01819","title":"M$^3$AD: Multi-task Multi-gate Mixture of Experts for Alzheimer's Disease Diagnosis with Conversion Pattern Modeling","abstract":"Alzheimer's disease (AD) progression follows a complex continuum from normal cognition (NC) through mild cognitive impairment (MCI) to dementia, yet most deep learning approaches oversimplify this into discrete classification tasks. This study introduces M$^3$AD, a novel multi-task multi-gate mixture of experts framework that jointly addresses diagnostic classification and cognitive transition modeling using structural MRI. We incorporate three key innovations: (1) an open-source T1-weighted sMRI preprocessing pipeline, (2) a unified learning framework capturing NC-MCI-AD transition patterns with demographic priors (age, gender, brain volume) for improved generalization, and (3) a customized multi-gate mixture of experts architecture enabling effective multi-task learning with structural MRI alone. The framework employs specialized expert networks for diagnosis-specific pathological patterns while shared experts model common structural features across the cognitive continuum. A two-stage training protocol combines SimMIM pretraining with multi-task fine-tuning for joint optimization. Comprehensive evaluation across six datasets comprising 12,037 T1-weighted sMRI scans demonstrates superior performance: 95.13% accuracy for three-class NC-MCI-AD classification and 99.15% for binary NC-AD classification, representing improvements of 4.69% and 0.55% over state-of-the-art approaches. The multi-task formulation simultaneously achieves 97.76% accuracy in predicting cognitive transition. Our framework outperforms existing methods using fewer modalities and offers a clinically practical solution for early intervention. Code: https://github.com/csyfjiang/M3AD.","short_abstract":"Alzheimer's disease (AD) progression follows a complex continuum from normal cognition (NC) through mild cognitive impairment (MCI) to dementia, yet most deep learning approaches oversimplify this into discrete classification tasks. This study introduces M$^3$AD, a novel multi-task multi-gate mixture of experts framewo...","url_abs":"https://arxiv.org/abs/2508.01819","url_pdf":"https://arxiv.org/pdf/2508.01819v1","authors":"[\"Yufeng Jiang\",\"Hexiao Ding\",\"Hongzhao Chen\",\"Jing Lan\",\"Xinzhi Teng\",\"Gerald W. Y. Cheng\",\"Zongxi Li\",\"Haoran Xie\",\"Jung Sun Yoo\",\"Jing Cai\"]","published":"2025-08-03T16:15:49Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[\"Mixture of Experts\"]","has_code":false,"code_links":[{"ID":611428,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2887371,"paper_url":"https://arxiv.org/abs/2508.01819","paper_title":"M$^3$AD: Multi-task Multi-gate Mixture of Experts for Alzheimer's Disease Diagnosis with Conversion Pattern Modeling","repo_url":"https://github.com/csyfjiang/M3AD","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}