Address the lack of medical data employing generative models to create high-resolution labeled datasets
Deep learning applications in the medical domain frequently encounter constraints due to insufficient data availability and imbalanced data distributions. These limitations can impede the development of robust models. Generative models like generative adversarial networks (GANs) present a promising avenue for generating realistic synthetic imaging data, thus mitigating issues related to data scarcity.
