Disentanglement learning to deconfound neuroimaging-environmental data: application to multi-site data harmonization in psychiatry
Disentanglement learning to deconfound neuroimaging-environmental data: application to multi-site data harmonization in psychiatry Ines W. Sampaio, Anna M. Bianchi, Stefan Borgwardt, … DOI: 10.1109/ACCESS.2026.3712218 Abstract Debiasing and deconfounding represent fundamental challenges in deep learning (DL) applications to neuroimaging data, where confounding effects can significantly compromise model reliability and generalizability. Traditional debiasing approaches often require preprocessing […]