Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

Make deep learning algorithms in computational pathology more reproducible and reusable

Nature Medicine volume 28, pages 1744–1746 (2022)Cite this article

Subjects

This article has been updated

Greater emphasis on reproducibility and reusability will advance computational pathology quickly and sustainably, ultimately optimizing clinical workflows and benefiting patient health.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Workflow in computational pathology.

Change history

  • 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).

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian Matek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sayedali Shetab Boushehri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Melanie Boxberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lorenz Lamm
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ario Sadafi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dominik J. E. Waibel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Carsten Marr
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Tingying Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-022-01905-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research