Make deep learning algorithms in computational pathology more reproducible and reusable

Greater emphasis on reproducibility and reusability will advance computational pathology quickly and sustainably, ultimately optimizing clinical workflows and benefiting patient health.

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Veröffentlicht in:Nature medicine Jg. 28; H. 9; S. 1744 - 1746
Hauptverfasser: Wagner, Sophia J., Matek, Christian, Shetab Boushehri, Sayedali, Boxberg, Melanie, Lamm, Lorenz, Sadafi, Ario, Waibel, Dominik J. E., Marr, Carsten, Peng, Tingying
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Nature Publishing Group US 01.09.2022
Nature Publishing Group
Schlagworte:
639/166/985
692/308/575
706/648/479
Algorithms
Artificial intelligence
Biomedical and Life Sciences
Biomedicine
Cancer Research
Comment
Computer applications
Deep learning
Digitization
Infectious Diseases
Machine learning
Metabolic Diseases
Molecular Medicine
Mutation
Neurosciences
Pathology
Publishing
Reproducibility
Subject specialists
ISSN:1078-8956, 1546-170X, 1546-170X
Online-Zugang:Volltext
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Zusammenfassung:Greater emphasis on reproducibility and reusability will advance computational pathology quickly and sustainably, ultimately optimizing clinical workflows and benefiting patient health.
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SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1078-8956
1546-170X
1546-170X
DOI:10.1038/s41591-022-01905-0