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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Vydané v:	Journal of magnetic resonance imaging Ročník 55; číslo 3; s. 908 - 916
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA John Wiley & Sons, Inc 01.03.2022 Wiley Subscription Services, Inc
Predmet:	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
On-line prístup:	Získať plný text
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