Deep Generative Medical Image Harmonization for Improving Cross‐Site Generalization in Deep Learning Predictors

Background In the medical imaging domain, deep learning‐based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an establi...

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Bibliographic Details
Published in:	Journal of magnetic resonance imaging Vol. 55; no. 3; pp. 908 - 916
Main Authors:	Bashyam, Vishnu M., Doshi, Jimit, Erus, Guray, Srinivasan, Dhivya, Abdulkadir, Ahmed, Singh, Ashish, Habes, Mohamad, Fan, Yong, Masters, Colin L., Maruff, Paul, Zhuo, Chuanjun, Völzke, Henry, Johnson, Sterling C., Fripp, Jurgen, Koutsouleris, Nikolaos, Satterthwaite, Theodore D., Wolf, Daniel H., Gur, Raquel E., Gur, Ruben C., Morris, John C., Albert, Marilyn S., Grabe, Hans J., Resnick, Susan M., Bryan, Nick R., Wittfeld, Katharina, Bülow, Robin, Wolk, David A., Shou, Haochang, Nasrallah, Ilya M., Davatzikos, Christos
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.03.2022 Wiley Subscription Services, Inc
Subjects:	Adolescent Age Biomarkers Brain Brain - diagnostic imaging Deep Learning Domains Field strength Generative adversarial networks harmonization Histograms Humans Image Processing, Computer-Assisted - methods Magnetic resonance imaging Medical imaging Medical research Neuroimaging Population studies Prediction models Research Design Retrospective Studies StarGAN Statistical analysis Statistical tests StarGAN deep learning harmonization
ISSN:	1053-1807, 1522-2586, 1522-2586
Online Access:	Get full text
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