Back to Search Start Over

Deep learning in medical imaging and radiation therapy.

Authors :
Sahiner, Berkman
Pezeshk, Aria
Hadjiiski, Lubomir M.
Wang, Xiaosong
Drukker, Karen
Cha, Kenny H.
Summers, Ronald M.
Giger, Maryellen L.
Source :
Medical Physics; Jan2019, Vol. 46 Issue 1, pe1-e36, 36p
Publication Year :
2019

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00942405
Volume :
46
Issue :
1
Database :
Complementary Index
Journal :
Medical Physics
Publication Type :
Academic Journal
Accession number :
133988798
Full Text :
https://doi.org/10.1002/mp.13264