Abstract #0196

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

Bonian Lu¹, Rangaprakash Deshpande², Madhura Ingalhalikar³, and Gopikrishna Deshpande¹

¹Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States, ²Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Medical School and Harvard-MIT Health Sciences and Technology, Charlestown, MA, United States, ³Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, India

In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods and pre-processing approaches vary across sites and datasets, leading to poor diagnostic classification. Domain adaptation aims to improve the classification performance in target domain data by utilizing the knowledge learned from the source domain, and making the distributions of data in source and target domains as similar as possible. In this sense, domain adaptation is one method that can be used to achieve and optimize transfer learning by using different datasets.

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