Abstract #4352
Application-specific structural brain MRI harmonization
Lijun An1,2,3, Pansheng Chen1,2,3, Jianzhong Chen1,2,3, Christopher Chen4, Juan Helen Zhou2, and B.T. Thomas Yeo1,2,3,5,6
1Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore, 2Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore, 3N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore, 4Department of Pharmacology, National University of Singapore, Singapore, Singapore, 5NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore, 6Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
Synopsis
We propose a flexible application-specific harmonization framework utilizing downstream application performance to regularize the harmonization procedure. Our approach can be integrated with various deep learning models. Here, we apply our approach to the recently proposed conditional variational autoencoder (cVAE) harmonization model. Three datasets (ADNI, N=1735; AIBL, N=495; MACC, N=557) collected from three different continents were used for evaluation. Our results suggest our approach (AppcVAE) compares favorably with ComBat (named for “combating batch effects when combining batches”) and cVAE for improving downstream application performance.
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