A primer for understanding radiology articles about machine learning and deep learning.
Deep learning
Machine learning
Magnetic resonance imaging
Tomography,
X-ray computed
Journal
Diagnostic and interventional imaging
ISSN: 2211-5684
Titre abrégé: Diagn Interv Imaging
Pays: France
ID NLM: 101568499
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
13
07
2020
revised:
06
10
2020
accepted:
06
10
2020
pubmed:
31
10
2020
medline:
15
7
2021
entrez:
30
10
2020
Statut:
ppublish
Résumé
The application of machine learning and deep learning in the field of imaging is rapidly growing. Although the principles of machine and deep learning are unfamiliar to the majority of clinicians, the basics are not so complicated. One of the major issues is that commentaries written by experts are difficult to understand, and are not primarily written for clinicians. The purpose of this article was to describe the different concepts behind machine learning, radiomics, and deep learning to make clinicians more familiar with these techniques.
Identifiants
pubmed: 33121910
pii: S2211-5684(20)30246-1
doi: 10.1016/j.diii.2020.10.001
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
765-770Informations de copyright
Copyright © 2020 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.