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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Podrobná bibliografia
Vydané v:	Nature medicine Ročník 28; číslo 9; s. 1744 - 1746
Hlavní autori:	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:	English
Vydavateľské údaje:	New York Nature Publishing Group US 01.09.2022 Nature Publishing Group
Predmet:	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
On-line prístup:	Získať plný text
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