Skip to main content

Thank you for visiting 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.

Make deep learning algorithms in computational pathology more reproducible and reusable

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

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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


  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  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).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  14. Wagner, S. J. et al. Preprint at medRxiv (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).

    CAS  Article  Google Scholar 

Download references


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

Authors and Affiliations



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

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).

Download citation

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing