关键词: 医学图像处理, 生成对抗网络, 生成模型, 判别模型

Abstract:

Generative Adversarial Nets（GANs） models can learn more abundant data information in unsupervised learning. GANs consist of a generator and a discriminator, and these two are alternately optimized through mutual games in the training of the confrontation to improve performance. In view of the problems of traditional generative confrontation network, such as gradient disappearance, mode collapse and inability to generate discrete data distribution, the researchers have proposed a number variations of GANs model. The paper describes the theory and structure of the GANs model. Then, the paper introduces several typical variant models, and elaborates the current research progress and status of the GANs model in image generation, image segmentation, image classification, target detection applications and super resolution image reconstruction. The in-depth analysis is carried out based on the research status and existing problems in the paper, and the future development trend and challenges of deep learning in the field of medical image processing are further summarized and discussed.

Key words: medical image processing, generative adversarial networks, generative model, discriminative model