Asurvey: Generative adversarial networks and their applications in medical imaging: an overview from computer science perspective

The lack of high-quality annotated medical image datasets is a major problem colliding with and hindering the growth of applications acquiring engines in the field of medical image analysis. In biomedical image analysis, the applicability of deep acquisition methods is directly influenced by the wealth of accessible image information. This is because the deep acquisition version requires a large image dataset to perform well. Generative Adversarial Networks (GANs) are widely used to remove data limitations in the generation of artificial biomedical images. As already mentioned, the artificial image is built by the feedback received. A discriminator is a pattern that artificially or realistically classifies an image and provides feedback to the generator. A general study is carried out on GANs network application to medical image segmentation, primarily adopted to several GANs-based versions, performance metrics, less operates, datasets, augmentation methods, money performance, and source codes. Secondly, this money offers a comprehensive overview of GANs network applications in different human diseases segmentation. We complete our research with vital discussion, limitations of GANs, and prepositions for coming directions. We hope this study is helpful and increases the sensitivity of GANs network implementations for biomedical image segmentation missions.