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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Abstract Greater emphasis on reproducibility and reusability will advance computational pathology quickly and sustainably, ultimately optimizing clinical workflows and benefiting patient health.
AbstractList Greater emphasis on reproducibility and reusability will advance computational pathology quickly and sustainably, ultimately optimizing clinical workflows and benefiting patient health.
Author Wagner, Sophia J.
Lamm, Lorenz
Sadafi, Ario
Shetab Boushehri, Sayedali
Marr, Carsten
Peng, Tingying
Waibel, Dominik J. E.
Matek, Christian
Boxberg, Melanie
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  organization: Institute of AI for Health, Helmholtz Munich – German Research Center for Environmental Health, Institute of Pathology, University Hospital Erlangen
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  givenname: Sayedali
  orcidid: 0000-0003-3391-9294
  surname: Shetab Boushehri
  fullname: Shetab Boushehri, Sayedali
  organization: Institute of AI for Health, Helmholtz Munich – German Research Center for Environmental Health, Department of Mathematics, Technical University of Munich, Data Science, Pharmaceutical Research and Early Development Informatics (pREDi), Roche Innovation Center Munich (RICM)
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  givenname: Melanie
  surname: Boxberg
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  surname: Lamm
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  orcidid: 0000-0002-3753-8598
  surname: Waibel
  fullname: Waibel, Dominik J. E.
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  surname: Marr
  fullname: Marr, Carsten
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  givenname: Tingying
  orcidid: 0000-0002-7881-1749
  surname: Peng
  fullname: Peng, Tingying
  email: tingying.peng@helmholtz-munich.de
  organization: Helmholtz AI, Helmholtz Munich – German Research Center for Environmental Health, Institute of AI for Health, Helmholtz Munich – German Research Center for Environmental Health
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Cites_doi 10.1101/2022.05.15.22275108
10.1038/s41591-019-0548-6
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  doi: 10.1038/s43018-020-0085-8
– volume: 1
  start-page: 538
  year: 2019
  ident: 1905_CR5
  publication-title: Nat. Mach. Intell.
  doi: 10.1038/s42256-019-0101-9
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Snippet Greater emphasis on reproducibility and reusability will advance computational pathology quickly and sustainably, ultimately optimizing clinical workflows and...
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StartPage 1744
SubjectTerms 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
Title Make deep learning algorithms in computational pathology more reproducible and reusable
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Volume 28
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