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.

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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