Guest Editorial Skin Image Analysis in the Age of Deep Learning

Abstract

The papers in this special section focus on skin image analysis using deep learning applications. Skin is the largest organ of the human body, and is the first area of a patient assessed by clinical staff. The skin delivers numerous insights into a patient’s underlying health: for example, pale or blue skin suggests respiratory issues, unusually yellowish skin can signal hepatic issues, or certain rashes can be indicative of autoimmune issues. Dermatological complaints are the most prevalent reason that patients seek primary care, and images of the skin are the most easily captured form of medical image in healthcare. However, certain serious skin diseases are not reliably diagnosed by primary care. Out of all medical imaging datasets, skin images are the most similar to other standard computer vision datasets. However, significant and unique challenges still exist in this domain. In addition, there are remarkable visual similarities among skin diseases, and compared to other medical imaging domains, varying genetics, disease states, imaging equipment, and imaging conditions can significantly alter the appearance of the skin, making automated analysis in this domain highly challenging.