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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Bibliographic Details
Published in	Nature medicine Vol. 28; no. 9; pp. 1744 - 1746
Main Authors	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
Language	English
Published	New York Nature Publishing Group US 01.09.2022 Nature Publishing Group
Subjects	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
Online Access	Get full text
ISSN	1078-8956 1546-170X 1546-170X
DOI	10.1038/s41591-022-01905-0

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Summary:	Greater emphasis on reproducibility and reusability will advance computational pathology quickly and sustainably, ultimately optimizing clinical workflows and benefiting patient health.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1078-8956 1546-170X 1546-170X
DOI:	10.1038/s41591-022-01905-0