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

摘要：

生成对抗网络（Generative Adversarial Nets，GANs）模型可以无监督学习到更丰富的数据信息，其包括生成模型与判别模型，凭借二者之间的对抗提高性能。针对传统GANs存在着梯度消失、模式崩溃及无法生成离散数据分布等问题，已经提出了大量的变体模型。介绍了生成对抗网络模型的理论和组成结构；介绍了几种典型的变体模型，重点介绍了生成对抗网络模型在图像生成、图像分割、图像分类、目标检测及图像超分辨率重建应用领域上的研究进展及现状。在研究现状和问题基础上进行了深入分析，进一步总结和探讨了GANs模型在医学图像处理领域中未来发展的趋势和所面临的挑战。

关键词: 医学图像处理, 生成对抗网络, 生成模型, 判别模型