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Make deep learning algorithms in computational pathology more reproducible and reusable

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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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Fig. 1: Workflow in computational pathology.

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  • 11 August 2022

    In the version of this article initially published, text in the second and third sections of Fig. 1 were obscured and have now been restored in the HTML and PDF versions of the article as of 11 August 2022

References

  1. Fuchs, T. J. & Buhmann, J. M. Comput. Med. Imaging Graph. 35, 515–530 (2011).

    Article  Google Scholar 

  2. Försch, S. et al. Dtsch. Arztebl. Int. 118, 194–204 (2021).

    PubMed  Google Scholar 

  3. Esteva, A. et al. Nature 542, 115–118 (2017).

    Article  CAS  Google Scholar 

  4. Coudray, N. et al. Nat. Med. 24, 1559–1567 (2018).

    Article  CAS  Google Scholar 

  5. Matek, C. et al. Nat. Mach. Intell. 1, 538–544 (2019).

    Article  Google Scholar 

  6. Lu, M. Y. et al. Nature 594, 106–110 (2021).

    Article  CAS  Google Scholar 

  7. Fu, Y. et al. Nat. Cancer 1, 800–810 (2020).

    Article  CAS  Google Scholar 

  8. Echle, A. et al. Br. J. Cancer 124, 686–696 (2021).

    Article  Google Scholar 

  9. van der Laak, J. et al. Nat. Med. 27, 775–784 (2021).

    Article  Google Scholar 

  10. Hutson, M. Science 359, 725–726 (2018).

    Article  Google Scholar 

  11. Stodden, V. et al. Science 354, 1240–1241 (2016).

    Article  CAS  Google Scholar 

  12. Haibe-Kains, B. et al. Nature 586, E14–E16 (2020).

    Article  CAS  Google Scholar 

  13. Howard, F. M. et al. Nat. Commun. 12, 4423 (2021).

    Article  CAS  Google Scholar 

  14. Wagner, S. J. et al. Preprint at medRxiv https://doi.org/10.1101/2022.05.15.22275108 (2022).

  15. Pineau, J. et al. J. Mach. Learn. 22, 1–20 (2021).

    Google Scholar 

  16. McDermott, M. B. A. et al. Sci. Transl. Med. 13, eabb1655 (2021).

    Article  Google Scholar 

  17. Wiens, J. et al. Nat. Med. 25, 1337–1340 (2019).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank P. Schüffler for feedback. S.J.W., L.L. and S.S.B. are supported by the Helmholtz Association under the joint research school “Munich School for Data Science - MUDS”. S.J.W., L.L. and T.P. were funded by Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI. S.S.B. has received funding by F. Hoffmann-la Roche LTD (No grant number is applicable). L.L. acknowledges a fellowship from the Boehringer Ingelheim Fonds. C.M. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant no. 866411).

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Author notes

  1. These authors contributed equally: Sophia J. Wagner, Christian Matek.

  2. These authors jointly supervised this work: Carsten Marr, Tingying Peng.

Authors and Affiliations

  1. Helmholtz AI, Helmholtz Munich – German Research Center for Environmental Health, Neuherberg, Germany

    Sophia J. Wagner, Lorenz Lamm, Carsten Marr & Tingying Peng

  2. Department of Informatics, Technical University of Munich, Garching, Germany

    Sophia J. Wagner, Lorenz Lamm & Ario Sadafi

  3. Institute of AI for Health, Helmholtz Munich – German Research Center for Environmental Health, Neuherberg, Germany

    Christian Matek, Sayedali Shetab Boushehri, Ario Sadafi, Dominik J. E. Waibel, Carsten Marr & Tingying Peng

  4. Institute of Pathology, University Hospital Erlangen, Erlangen, Germany

    Christian Matek

  5. Department of Mathematics, Technical University of Munich, Garching, Germany

    Sayedali Shetab Boushehri

  6. Data Science, Pharmaceutical Research and Early Development Informatics (pREDi), Roche Innovation Center Munich (RICM), Penzberg, Germany

    Sayedali Shetab Boushehri

  7. Institute of Pathology, Technical University Munich, Munich, Germany

    Melanie Boxberg

  8. Institute of Pathology Munich-North, Munich, Germany

    Melanie Boxberg

  9. Helmholtz Pioneer Campus, Helmholtz Munich – German Research Center for Environmental Health, Neuherberg, Germany

    Lorenz Lamm

  10. School of Life Sciences, Technical University of Munich, Weihenstephan, Germany

    Dominik J. E. Waibel

Authors
  1. Sophia J. Wagner
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  2. Christian Matek
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  3. Sayedali Shetab Boushehri
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  4. Melanie Boxberg
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  5. Lorenz Lamm
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  6. Ario Sadafi
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  7. Dominik J. E. Waibel
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  8. Carsten Marr
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  9. Tingying Peng
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Contributions

S.J.W., Ch.M., T.P. and C.M. wrote the manuscript. All authors contributed to the review14 in preparation for this Comment.

Corresponding authors

Correspondence to Carsten Marr or Tingying Peng.

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The authors declare no competing interests.

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Wagner, S.J., Matek, C., Shetab Boushehri, S. et al. Make deep learning algorithms in computational pathology more reproducible and reusable. Nat Med 28, 1744–1746 (2022). https://doi.org/10.1038/s41591-022-01905-0

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