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

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 . L.L. acknowledges a fellowship from the Boehringer Ingelheim Fonds. C.M.

Christian Matek, Sayedali Shetab Boushehri, Ario Sadafi, Dominik J. E. Waibel, Carsten Marr & Tingying PengDepartment of Mathematics, Technical University of Munich, Garching, GermanyData Science, Pharmaceutical Research and Early Development Informatics , Roche Innovation Center Munich , Penzberg, GermanyMelanie BoxbergHelmholtz Pioneer Campus, Helmholtz Munich – German Research Center for Environmental Health, Neuherberg, GermanyDominik J. E.

p_tingying MarrCarsten helmholtz_ai ‘Transfer learning’ an option for advancing applicability for all

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