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Deep learning in medical imaging and radiation therapy
Author(s) -
Sahiner Berkman,
Pezeshk Aria,
Hadjiiski Lubomir M.,
Wang Xiaosong,
Drukker Karen,
Cha Kenny H.,
Summers Ronald M.,
Giger Maryellen L.
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13264
Subject(s) - medical imaging , medical radiation , deep learning , medical physicist , medical physics , convolutional neural network , radiation therapy , computer science , artificial intelligence , data science , medicine , radiology
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.