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
ISSN:	1078-8956, 1546-170X, 1546-170X
Online Access:	Get full text
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