Deep learning in medical imaging and radiation therapy
Berkman SahinerDIDSR/OSEL/CDRH U.S. Food and Drug Administration Silver Spring MD 20993 USAAria PezeshkDIDSR/OSEL/CDRH U.S. Food and Drug Administration Silver Spring MD 20993 USALubomir M. HadjiiskiDepartment of Radiology University of Michigan Ann Arbor MI 48109 USAXiaosong WangImaging Biomarkers and Computer‐aided Diagnosis Lab Radiology and Imaging Sciences NIH Clinical Center Bethesda MD 20892‐1182 USAKaren DrukkerDepartment of Radiology University of Chicago Chicago IL 60637 USAKenny H. ChaDIDSR/OSEL/CDRH U.S. Food and Drug Administration Silver Spring MD 20993 USARonald M. SummersImaging Biomarkers and Computer‐aided Diagnosis Lab Radiology and Imaging Sciences NIH Clinical Center Bethesda MD 20892‐1182 USAMaryellen L. GigerDepartment of Radiology University of Chicago Chicago IL 60637 USA
2018en
ABI
Annotatsiya
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) 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 dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.
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