针对阿尔茨海默病影像缺失问题，提出可灵活融合多模态数据的新模型。
Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
- 为每种影像模态设计独立路由，提升缺失数据下的融合灵活性。
- 在模态缺失率高达80%时仍保持领先预测性能，优于现有最优模型。
- 适合临床实际中多模态数据不全的场景，尤其对影像采集不完整的患者有用。
阿尔茨海默病（AD）是一种进展性神经退行性疾病，患者认知衰退速度差异大。准确预测病情进展需融合多种神经影像数据。然而，现有多模态模型在推理时若缺失大量模态（常见于临床），性能显著下降。为此，本文提出PerM-MoE，一种新型稀疏专家混合方法，用每个模态独立的路由器替代传统单一路由器。基于阿尔茨海默病神经影像计划（ADNI）数据，使用T1加权MRI、FLAIR、淀粉样蛋白β PET和τ PET影像，评估PerM-MoE、当前最优的Flex-MoE及单模态模型在不同模态缺失率下预测两年内临床痴呆量表-总分（CDR-SB）变化的表现。结果表明，PerM-MoE在多数缺失场景下超越现有方法，且专家利用效率高于Flex-MoE。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a progressive neurodegenerative disease with high inter-patient variance in rate of cognitive decline. AD progression prediction aims to forecast patient cognitive decline and benefits from incorporating multiple neuroimaging modalities. However, existing multimodal models fail to make accurate predictions when many modalities are missing during inference, as is often the case in clinical settings. To increase multimodal model flexibility under high modality missingness, we introduce PerM-MoE, a novel sparse mixture-of-experts method that uses independent routers for each modality in place of the conventional, single router. Using T1-weighted MRI, FLAIR, amyloid beta PET, and tau PET neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we evaluate PerM-MoE, state-of-the-art Flex-MoE, and unimodal neuroimaging models on predicting two-year change in Clinical Dementia Rating-Sum of Boxes (CDR-SB) scores under varying levels of modality missingness. PerM-MoE outperforms the state of the art in most variations of modality missingness and demonstrates more effective utility of experts than Flex-MoE.
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