Machine (Deep) learning methods for image processing and radiomics

Abstract

Methods from the field of machine (deep) learning have been successful in tackling a number of tasks in medical imaging, from image reconstruction or processing to predictive modeling, clinical planning and decision-aid systems. The ever growing availability of data and the improving ability of algorithms to learn from them has led to the rise of methods based on neural networks to address most of these tasks with higher efficiency and often superior performance than previous, 'shallow' machine learning methods. The present editorial aims at contextualizing within this framework the recent developments of these techniques, including these described in the papers published in the present special issue on machine (deep) learning for image processing and radiomics in radiation-based medical sciences.