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.
@article{arxiv.2601.21076,
title = {Multi-modal Imputation for Alzheimer's Disease Classification},
author = {Abhijith Shaji and Tamoghna Chattopadhyay and Sophia I. Thomopoulos and Greg Ver Steeg and Paul M. Thompson and Jose-Luis Ambite},
journal= {arXiv preprint arXiv:2601.21076},
year = {2026}
}