Make deep learning algorithms in computational pathology more reproducible and reusable

Wagner SJ, Matek C, Shetab Boushehri S, Boxberg M, Lamm L, Sadafi A, Waibel DJE, Marr C, Peng T (2022)

Publication Type: Journal article, Editorial

Publication year: 2022

Journal

Nature Medicine Nature Publishing Group

DOI: 10.1038/s41591-022-01905-0

Abstract

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

Authors with CRIS profile

Christian Matek Institute of Pathology

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Wagner, S.J., Matek, C., Shetab Boushehri, S., Boxberg, M., Lamm, L., Sadafi, A.,... Peng, T. (2022). Make deep learning algorithms in computational pathology more reproducible and reusable. Nature Medicine. https://doi.org/10.1038/s41591-022-01905-0

MLA:

Wagner, Sophia J., et al. "Make deep learning algorithms in computational pathology more reproducible and reusable." Nature Medicine (2022).

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