Survey: application and analysis of generative adversarial networks in medical images
Yang Heng, Ma Yinghua, Fiaz Gul Khan, Ahmad Khan, Farman Ali, Ahmad Ali AlZubi, Hui Zeng
Abstract
Generative Adversarial Networks (GANs) have shown promising prospects and achieved significant results in medical image analysis tasks. This article provides a comprehensive review of recent research on GANs and their variants in medical applications, including tasks such as image synthesis, segmentation, classification, detection, denoising, reconstruction, fusion, registration, and prediction. We summarize and analyze the reviewed literature, with a focus on model framework design,dataset sources, and performance evaluation. Our research findings are presented in the form of tables. In the end,article discusses open challenges and directions for future research.