Generative Adversarial Networks (GANs) for Medical Image Processing: Recent Advancements

Generative Adversarial Networks (GANs) constitute an advanced category of deep learning models that have significantly transformed the domain of generative modelling. They demonstrate a profound capability to produce realistic and high-quality synthetic data across various domains. Recently, GANs have also emerged as a powerful and innovative approach in medical image processing. Numerous scholarly investigations consistently highlight the superiority of GAN-based methodologies in this context. The generation of realistic synthetic images aims to advance segmentation precision, augment image quality, and facilitate multimodal analysis. These enhancements significantly bolster the analytical capabilities of medical professionals, leading to more precise diagnostic evaluations and the formulation of personalized treatment plans, thereby contributing to improved patient prognosis. In this work, we rigorously review the latest advancements in the application of Generative Adversarial Networks (GANs) within the domain of medical imaging, encompassing research published between 2018 and 2024. The corpus of literature selected for this review is derived from the most relevant and authoritative databases, including Elsevier, Springer, IEEE Xplore, and Google Scholar, among others. This review rigorously evaluates scholarly publications employing Generative Adversarial Networks (GANs) for the synthesis and generation of medical images, segmentation of medical imaging data, image-to-image translation in medical contexts, and denoising or reconstruction of medical imagery. The findings of this review present a thorough synthesis of contemporary applications of Generative Adversarial Networks (GANs) in the domain of medical imaging. This investigation serves as a prospective reference in the realm of GAN utilization for medical image processing, offering guidance and insights for current and future research endeavors.