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Abstract
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to: 1) summarize what has been achieved to date; 2) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and 3) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for data set expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions. This article is protected by copyright. All rights reserved.

Author information

Sahiner B1, Pezeshk A1, Hadjiiski LM2, Wang X3, Drukker K4, Cha KH1, Summers RM3, Giger ML4.
1
DIDSR/OSEL/CDRH, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA.
2
Department of Radiology, University of Michigan, Ann Arbor, MI, 48109.
3
Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD, 20892-1182.
4
Department of Radiology, University of Chicago, Chicago, IL, 60637.