How Adversarial Networks and Deep Learning Could Help in Solving Problems of Big Medical Image Data

Abstract

The exponential growth of medical imaging data poses significant challenges for storage, analysis, and interpretation. This article explores how deep learning, particularly Generative Adversarial Networks (GANs), can be employed to tackle core issues such as data scarcity, annotation costs, data imbalance, and noise. By leveraging adversarial training and representation learning, GANs can synthesize realistic medical images, enhance image resolution, and assist in diagnosis. We discuss practical applications, challenges, and future perspectives for integrating deep learning models in medical image analysis pipelines.