Big data in health care: Opportunities, challenges and future direction Vijayalakshmi Pasupathy, Rashmita Khilar Advanced Machine Learning for Complex Medical Data Analysis, 2025 Recently different deep-learning algorithms have emerged. But the one that is most promising in medical imaging is the Generative Adversarial Network. In the artificial intelligence field, GANs represent a significant area of research due to their extensive capacity to generate sophisticated data, which has garnered significant attention. The GAN comprises two key deep networks: a generator and a discriminator. The discriminator's role is to distinguish between real images and synthetic images produced by the generator. GANs are typically utilized in medical imaging for two distinct functions. Primarily, the generative component is the focus of attention, since it can help in learning about a generation of new images as well as analyzing and exposing the fundamental framework of the training data. This GAN feature highlights the confidentiality of patient records and also reduces the scarcity of medical data. Secondarily, the discriminative component is the focus of attention in which the aberrant images are provided to the discriminator D, which serves as a detector because it can be thought of as a learned reference for normal images. This chapter focuses on the contemporary evolutions in generative models, especially in the arena of medicine. This article presents the various medical imaging techniques employed in different GAN architectures and the uses of GAN in medical imaging. This article's goal is to give an in-depth comprehension of the GAN and its different architectures and applications of GAN in medical imaging.
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