Generative Adversarial Networks and Its Applications in Biomedical Informatics

The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn the generative model of any data distribution...

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Published inFrontiers in public health Vol. 8; p. 164
Main Authors Lan, Lan, You, Lei, Zhang, Zeyang, Fan, Zhiwei, Zhao, Weiling, Zeng, Nianyin, Chen, Yidong, Zhou, Xiaobo
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 12.05.2020
Subjects
biomedical applications
Computational Biology
data augmentation
discriminator
Generative Adversarial Networks (GAN)
generator
image conversion
Image Processing, Computer-Assisted
Medical Informatics
Neural Networks, Computer
Public Health
generator
data augmentation
image conversion
Generative Adversarial Networks (GAN)
biomedical applications
discriminator
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Summary:The basic Generative Adversarial Networks (GAN) model is composed of the input vector, generator, and discriminator. Among them, the generator and discriminator are implicit function expressions, usually implemented by deep neural networks. GAN can learn the generative model of any data distribution through adversarial methods with excellent performance. It has been widely applied to different areas since it was proposed in 2014. In this review, we introduced the origin, specific working principle, and development history of GAN, various applications of GAN in digital image processing, Cycle-GAN, and its application in medical imaging analysis, as well as the latest applications of GAN in medical informatics and bioinformatics.
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Reviewed by: Robertas Damasevicius, Kaunas University of Technology, Lithuania; Fuhai Li, Washington University in St. Louis, United States
Edited by: Shuihua Wang, University of Leicester, United Kingdom
These authors have contributed equally to this work
This article was submitted to Digital Public Health, a section of the journal Frontiers in Public Health
ISSN:2296-2565
2296-2565
DOI:10.3389/fpubh.2020.00164