Generative Adversarial Networks and Its Applications in Biomedical Informatics

Frontiers in Public Health

Xiaobo Zhou¹  Lei You¹  Weiling Zhao¹  Yidong Chen²  Zeyang Zhang³  Zhiwei Fan⁴  Nianyin Zeng⁵  Lan Lan⁶ 

[1] Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States;Department of Computer Science and Technology, College of Computer Science, Sichuan University, Chengdu, China;Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China;Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China;Department of Instrumental and Electrical Engineering, Xiamen University, Fujian, China;West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China;

关键词: Generative Adversarial Networks (GAN);    generator;    discriminator;    data augmentation;    image conversion;    biomedical applications;   

DOI  :  10.3389/fpubh.2020.00164

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown