Generative Adversarial Networks in Medical Image Analysis: A Comprehensive Survey

Recently, researchers have introduced numerous techniques from other domains into medical image processing to improve its efficacy and effectiveness to a certain point. Generative Adversarial Networks (GANs) have discovered a pivotal role within medical imaging, revolutionizing various applications that span diagnostics, image synthesis, and data augmentation. This overview explores GANs’ manifold applications in medical imaging, showcasing their proficiency in enhancing disease detection, creating lifelike medical images for training, overcoming data scarcity, and tailoring treatment approaches to GANs’ capacity to produce highly authentic images that mirror actual patient data while safeguarding privacy holds the potential to propel medical research and clinical practice to new heights. This study evaluated the present state of the art in this area. The core notions of the GAN were initially introduced. The GAN’s applications were then summarized in terms of medical image denoising, detection, segmentation, synthesis, reconstruction, and classification. Finally, future research opportunities in this area were explored.