• Gabriel Chartrand, Phillip M Cheng, Eugene Vorontsov, Michal Drozdzal, Simon Turcotte, Christopher J Pal, Samuel Kadoury, and An Tang.
• From the Departments of Radiology (G.C., E.V., A.T.) and Hepatopancreatobiliary Surgery (S.T.), Centre Hospitalier de l'Université de Montréal, Hôpital Saint-Luc, 850 rue Saint-Denis, Montréal, QC, Canada H2X 0A9; Imagia Cybernetics, Montréal, Québec, Canada (G.C., M.D.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (P.M.C.); Montreal Institute for Learning Algorithms, Montréal, Québec, Canada (E.V., M.D., C.J.P.); École Polytechnique, Montréal, Québec, Canada (E.V., C.J.P., S.K.); Department of Surgery, University of Montreal, Montréal, Québec, Canada (S.T.); and Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada (S.T., S.K., A.T.).
• Radiographics. 2017 Nov 1; 37 (7): 2113-2131.

AbstractDeep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. ©RSNA, 2017.

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