Towards Synthetic Generation of Clinical Rosacea Images with GAN Models

Abstract

Computer-aided skin disease diagnosis has recently attracted much attention in the scientific and medical research community due to advances in computer vision and machine learning algorithms. These methodologies essentially rely on large datasets collected from hospitals and medical professionals. Data scarcity is a vital problem in the medical domain, especially facial skin conditions, due to privacy concerns. For instance, some facial skin conditions, e.g. Rosacea, require observation of the entire face, which reveals the patient's identity. Rosacea is a lamentably neglected skin condition in the computer-aided diagnosis research community, due to the limited availability of Rosacea datasets. Hence, there is a need for exploring alternative ways to deal with the limited available data for Rosacea. A common approach to expanding small datasets is to utilise augmentation techniques. One of the most powerful augmentation methods in machine learning is Generative Adversarial Networks (GANs). Recently, GANs, principally the variants of StyleGAN, have successfully generated synthetic facial images. In this paper, a small dataset of a particular skin disease, Rosacea, with 300 images is used to examine the potential of a variant of StyleGAN known as StyleGAN2-ADA. The preliminary experiments and evaluations show promising signs towards addressing the data scarcity for computer-aided Rosacea diagnosis.