[Artificial intelligence and machine learning in oncologic imaging].

Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung.
Computer-assisted image processing Deep learning Diagnostic imaging Machine learning Neural networks (computer)

Journal

Der Pathologe
ISSN: 1432-1963
Titre abrégé: Pathologe
Pays: Germany
ID NLM: 8006541

Informations de publication

Date de publication:
Nov 2020
Historique:
pubmed: 15 10 2020
medline: 8 1 2021
entrez: 14 10 2020
Statut: ppublish

Résumé

Machine learning (ML) is entering many areas of society, including medicine. This transformation has the potential to drastically change medicine and medical practice. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers-in collaboration with humans or alone-have been outperforming humans regarding tumor identification, tumor classification, estimating prognoses, and evaluation of treatments. In addition, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and inexpensive manner. In the future, artificial intelligence applications will become an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.

Identifiants

pubmed: 33052431
doi: 10.1007/s00292-020-00827-3
pii: 10.1007/s00292-020-00827-3
doi:

Types de publication

Journal Article

Langues

ger

Sous-ensembles de citation

IM

Pagination

649-658

Auteurs

Jens Kleesiek (J)

AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland. jens.kleesiek@uk-essen.de.
German Cancer Consortium (DKTK), Heidelberg, Deutschland. jens.kleesiek@uk-essen.de.
Institut für Künstliche Intelligenz in der Medizin (IKIM), Universitätsklinikum Essen, Girardetstr. 6, 45131, Essen, Deutschland. jens.kleesiek@uk-essen.de.

Jacob M Murray (JM)

AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland.
Heidelberg University, Heidelberg, Deutschland.

Christian Strack (C)

AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland.
Heidelberg University, Heidelberg, Deutschland.

Sebastian Prinz (S)

AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland.
Heidelberg University, Heidelberg, Deutschland.

Georgios Kaissis (G)

Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, München, Deutschland.

Rickmer Braren (R)

German Cancer Consortium (DKTK), Heidelberg, Deutschland.
Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, München, Deutschland.

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Classifications MeSH