DeepResBat: deep residual batch harmonization accounting for covariate distribution differences
Lijun An,
Chen Zhang,
Naren Wulan
et al.
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, cu… Show more
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