生成对抗网络医学图像去噪研究综述

doi:10.3969/j.issn.0258-8021.2022.06.010

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Abstract As an important means to assist the detection of diseases and make better diagnostic decisions, the definition of medical image is of great significance for clinical treatment. The unique confrontation training idea of generative adversarial network (GAN) can generate high-quality samples. The success in the field of computer vision makes GAN a bright prospect. In this article, the application of GAN in medical image denoising was reviewed. Firstly, the basic theory, advantages and disadvantages of GAN were introduced. Then, the derivation model of GAN for medical image denoising was introduced in detail, and various loss functions that can help improve the denoising performance of GAN for medical images were summarized. And other deep learning frameworks, which can be nested into the GAN model and play an auxiliary role in medical image denoising, were presented as well. The methods to improve the performance of GAN network for medical image denoising were summarized. Finally, the application prospects, challenges and possible future research directions of GAN in medical image denoising were discussed.

Key words： generative adversarial networks (GANs) deep learning noise medical image denoising loss function

Received: 13 October 2021

PACS:

R318

Corresponding Authors: ^*E-mail: xuzhenghua1987@gmail.com

About author:: ^#Member, Chinese Society of Biomedical Engineering

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Cite this article:

Yu Miao,Xu Zhenghua. A Review on Generative Adversarial Networks in Medical Image[J]. Chinese Journal of Biomedical Engineering, 2022, 41(6): 724-731.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2022.06.010 OR http://cjbme.csbme.org/EN/Y2022/V41/I6/724

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