Multi-modal Imputation for Alzheimer's Disease Classification

• URL: http://arxiv.org/abs/2601.21076v1
• Date: Wed, 28 Jan 2026 22:02:45 GMT
• Title: Multi-modal Imputation for Alzheimer's Disease Classification
• Authors: Abhijith Shaji, Tamoghna Chattopadhyay, Sophia I. Thomopoulos, Greg Ver Steeg, Paul M. Thompson, Jose-Luis Ambite,
• Abstract summary: We use conditional denoising diffusion probabilistic model to impute missing DWI scans from T1 scans.<n>We perform extensive experiments to evaluate whether such imputation improves the accuracy of uni-modal and bi-modal deep learning models.
• Score: 12.097204781416437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer's disease, from magnetic resonance imaging (MRI). Combining multiple imaging modalities, such as T1-weighted (T1) and diffusion-weighted imaging (DWI) scans, can increase diagnostic performance. However, complete multimodal datasets are not always available. We use a conditional denoising diffusion probabilistic model to impute missing DWI scans from T1 scans. We perform extensive experiments to evaluate whether such imputation improves the accuracy of uni-modal and bi-modal deep learning models for 3-way Alzheimer's disease classification-cognitively normal, mild cognitive impairment, and Alzheimer's disease. We observe improvements in several metrics, particularly those sensitive to minority classes, for several imputation configurations.

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