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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

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

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Correspondence to Carsten Marr or Tingying Peng.

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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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