Multi-modal Imputation for Alzheimer's Disease Classification

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Shaji, Abhijith, Chattopadhyay, Tamoghna, Thomopoulos, Sophia I., Steeg, Greg Ver, Thompson, Paul M., Ambite, Jose-Luis
Format:	Preprint
Publié:	2026
Sujets:	Artificial Intelligence
Accès en ligne:	Acceder al recurso
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author	Shaji, Abhijith Chattopadhyay, Tamoghna Thomopoulos, Sophia I. Steeg, Greg Ver Thompson, Paul M. Ambite, Jose-Luis
author_facet	Shaji, Abhijith Chattopadhyay, Tamoghna Thomopoulos, Sophia I. Steeg, Greg Ver Thompson, Paul M. Ambite, Jose-Luis
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2601_21076
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Multi-modal Imputation for Alzheimer's Disease Classification Shaji, Abhijith Chattopadhyay, Tamoghna Thomopoulos, Sophia I. Steeg, Greg Ver Thompson, Paul M. Ambite, Jose-Luis Artificial Intelligence 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.
title	Multi-modal Imputation for Alzheimer's Disease Classification
topic	Artificial Intelligence
url	https://arxiv.org/abs/2601.21076

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