• Geert Litjens, Thijs Kooi, Bejnordi Babak Ehteshami BE Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands., Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, van der Laak Jeroen A W M JAWM Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands., Bram van Ginneken, and Clara I Sánchez.
• Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: geert.litjens@radboudumc.nl.
• Med Image Anal. 2017 Dec 1; 42: 60-88.

AbstractDeep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.Copyright © 2017 Elsevier B.V. All rights reserved.

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