VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data.

ABSTRACT: In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural differences between groups and leads to poor machine learning based diagnostic classification of neurodevelopmental conditions. This could be potentially addressed by domain adaptation, which aims to improve classification performance in a given target domain by utilizing the knowledge learned from a different source domain by making data distributions of the two domains as similar as possible. In order to demonstrate the utility of domain adaptation for multi-site fMRI data, this research developed a variational autoencoder-maximum mean discrepancy (VAE-MMD) deep learning model for three-way diagn

SUBMITTER: Deshpande G 

PROVIDER: S-EPMC12460464 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature