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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Vydáno v:Nature medicine Ročník 28; číslo 9; s. 1744 - 1746
Hlavní autoři: Wagner, Sophia J., Matek, Christian, Shetab Boushehri, Sayedali, Boxberg, Melanie, Lamm, Lorenz, Sadafi, Ario, Waibel, Dominik J. E., Marr, Carsten, Peng, Tingying
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Nature Publishing Group US 01.09.2022
Nature Publishing Group
Témata:
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
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Shrnutí:Greater emphasis on reproducibility and reusability will advance computational pathology quickly and sustainably, ultimately optimizing clinical workflows and benefiting patient health.
Bibliografie:ObjectType-Article-1
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ISSN:1078-8956
1546-170X
1546-170X
DOI:10.1038/s41591-022-01905-0