Make deep learning algorithms in computational pathology more reproducible and reusable,Nature Medicine

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Make deep learning algorithms in computational pathology more reproducible and reusable
Nature Medicine ( IF 58.7 ) Pub Date : 2022-08-08 , DOI: 10.1038/s41591-022-01905-0
Sophia J Wagner _{1,

2} , Christian Matek _{3,

4} , Sayedali Shetab Boushehri _{3,

5,

6} , Melanie Boxberg _{7,

8} , Lorenz Lamm _{1,

2,

9} , Ario Sadafi _{2,

3} , Dominik J E Waibel _{3,

10} , Carsten Marr _{1,

3} , Tingying Peng _{1,

3}

Affiliation

Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
Department of Informatics, Technical University of Munich, Garching, Germany.
Institute of AI for Health, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
Institute of Pathology, University Hospital Erlangen, Erlangen, Germany.
Department of Mathematics, Technical University of Munich, Garching, Germany.
Data Science, Pharmaceutical Research and Early Development Informatics (pREDi), Roche Innovation Center Munich (RICM), Penzberg, Germany.
Institute of Pathology, Technical University Munich, Munich, Germany.
Institute of Pathology Munich-North, Munich, Germany.
Helmholtz Pioneer Campus, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany.
School of Life Sciences, Technical University of Munich, Weihenstephan, Germany.

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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