DeepResBat: deep residual batch harmonization accounting for covariate distribution differences.

ABSTRACT: Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization outcomes. We propose two DNN-based harmonization approaches t

SUBMITTER: An L 

PROVIDER: S-EPMC10827218 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature