Skin disease analysis with limited data in particular Rosacea: a review and recommended framework

Recently, the rapid advancements in Deep Learning and Computer Vision technologies have introduced a new and exciting era in the field of skin disease analysis. However, there are certain challenges in the roadmap towards developing such technologies for real-life applications that must be investigated. This study considers one of the key challenges in data acquisition and computation, viz. data scarcity. Data scarcity is a central problem in acquiring medical images and applying machine learning techniques to train Convolutional Neural Networks for disease diagnosis. The main objective of this study is to explore the possible methods to deal with the data scarcity problem and to improve diagnosis with small datasets. The challenges in data acquisition for a few lamentably neglected skin conditions such as rosacea are an excellent instance to explore the possibilities of improving computer-aided skin disease diagnosis. With data scarcity in mind, the possible techniques explored and discussed include Generative Adversarial Networks, Meta-Learning, Few-Shot classification, and 3D face modelling. Furthermore, the existing studies are discussed based on skin conditions considered, data volume and implementation choices. Some future research directions are recommended

Towards Synthetic Generation of Clinical Rosacea Images with GAN Models

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