Leveraging Generative Adversarial Networks (GANs) for Enhancing Medical Data Through Augmentation

This paper focuses on the importance of Generative Adversarial Networks (GANs) in medical imaging. It describes the GAN architecture that includes a generator and a discriminator which work together to produce lifelike fake data. These kinds of models are particularly useful for medical imaging applications when there is a shortage of labeled data and they are thus very important in enhancing model generalization. Particularly when such models are trained on imbalanced datasets. GAN models have the capability to increase the amount of data available but also its rich diversity and better quality which is very vital for the performance of deep learning models in diagnostics. This paper emphasizes on the ways in which GANs are used in the fields such as disease diagnosis and image enhancement, etc. solving the problem of lack of annotated medical databases for training. In the case of medical images, of course, traditional data augmentation methods, like flipping or rotation are helpful however they are not capable of reaching the imager complexities. Such weaknesses are alleviated by GAN models that are capable of producing completely new and clinically-meaning images which assists in better image segmentation for disease prediction. Moreover, this paper provides insight into medical imaging data augmentation issues, such as how synthetic patient data holds ethical implications and the understanding of made up images.