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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Bibliographic Details
Published in:Frontiers in public health 2020-05, Vol.8, p.164-164
Main Authors: Lan, Lan, You, Lei, Zhang, Zeyang, Fan, Zhiwei, Zhao, Weiling, Zeng, Nianyin, Chen, Yidong, Zhou, Xiaobo
Format: Article
Language:English
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
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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.
ISSN:2296-2565
2296-2565
DOI:10.3389/fpubh.2020.00164