Machine learning and deep learning in medicine and neuroimaging

Abstract

Artificial intelligence is the science and engineering of machines that can mimic human intelligence. Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without being explicitly programmed. Deep learning algorithms learn by processing the data with increasing levels of abstraction in each layer. We present a narrative review of the relevant literature with a particular focus on deep learning for image classification and image segmentation in neuroimaging. For the first time in history, computers can automatically perform some clinically relevant tasks at the level, or even above the level, of the relevant medical specialists. A turning point in machine learning occurred in the 2010s as a result of (1) the multiple technical improvements that machine learning has been accumulating over several decades, (2) the exponential increase in computing power, and (3) the wide availability of very large databases with millions of observations and thousands of variables. Machine learning is starting to be successfully applied to several areas of medicine, including predictive analytics, decision support, natural language processing of free-text notes, and automatic interpretation of electrophysiological recordings. Among all the applications of machine learning in medicine, deep learning for computer vision is the one that has enjoyed the greatest success. The emphasis of this review is the application of convolutional neural networks for image classification and for image segmentation in neuroimaging. Machine learning and deep learning are increasingly integrated into the clinical workflow and applied in neuroimaging interpretation. Natural language processing is likely to gain increasing importance in medicine in the near future. Complex decision-making that mimics human thinking with reinforcement learning is still far away on the horizon.