Deep learning in medical imaging and radiation therapy.
computer-aided detection/characterization
deep learning, machine learning
reconstruction
segmentation
treatment
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Jan 2019
Jan 2019
Historique:
received:
04
01
2018
revised:
18
09
2018
accepted:
09
10
2018
pubmed:
28
10
2018
medline:
22
1
2019
entrez:
28
10
2018
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 30367497
doi: 10.1002/mp.13264
pmc: PMC9560030
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1-e36Subventions
Organisme : U.S. Food and Drug Administration
Organisme : NIH HHS
ID : CA 189240
Pays : United States
Organisme : NIH HHS
ID : CA 166945
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA189240
Pays : United States
Organisme : NIH HHS
ID : CA 195564
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA195564
Pays : United States
Organisme : The University of Chicago
ID : CTSA UL1 TR000430
Organisme : NCATS NIH HHS
ID : UL1 TR000430
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA166945
Pays : United States
Informations de copyright
© 2018 American Association of Physicists in Medicine.
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