Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey.

ABSTRACT: Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs

SUBMITTER: Iqbal A 

PROVIDER: S-EPMC9264294 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature