West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.
Front Public Health. 2020 May 12;8:164. doi: 10.3389/fpubh.2020.00164. eCollection 2020.
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.
基础生成对抗网络（GAN）模型由输入向量、生成器和判别器组成。其中，生成器和判别器是隐式函数表达式，通常通过深度神经网络实现。GAN 通过对抗方法学习任何数据分布的生成模型，具有出色的性能。自 2014 年提出以来，它已广泛应用于不同领域。在本文中，我们介绍了 GAN 的起源、具体工作原理和发展历史，以及 GAN 在数字图像处理、Cycle-GAN 及其在医学图像分析中的应用，以及 GAN 在医学信息学和生物信息学中的最新应用。
