Guest Editorial: Computer Vision in Cancer Data Analysis

Abstract

Recent progress in imaging hardware, acquisition techniques, and algorithmic processing of data have led to advances in detection, diagnosis, staging, treatment, and follow-up in cancer-related clinical workflows as well as fundamental understanding of cancer modelling and dynamics. Computer Vision presents a promising approach to process the ever-increasing amount of cancer-related data acquired and available through data repositories. The seven papers that constitute this Special Issue present a variety of algorithmic techniques, from texture analysis to adversarial auto-encoders, which in turn are applied to different imaging modalities, including microscopy, mammography, ultrasound, computed tomography and dermoscopy.