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DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible

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Fig. 1: The DL4MicEverywhere platform.

Code availability

The source code, documentation and tutorials for DL4MicEverywhere are available at under a Creative Commons CC-BY-4.0 license.


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We thank Amin Rezaei, Ainhoa Serrano, Pablo Alonso, Urtzi Beorlegui, Andoni Rodriguez, Erlantz Calvo, Soham Mandal and Virginie Uhlmann for their contributions to the ZeroCostDL4Mic notebook collection. I.H.-C., M.G.F., C.T.R., R.H. and E.G.-d.-M. received funding from the European Union through the Horizon Europe program (AI4LIFE project with grant agreement 101057970-AI4LIFE and RT-SuperES project with grant agreement 101099654-RTSuperES to R.H.). I.H.-C., M.G.F., E.G.-d.-M. and R.H. also acknowledge the support of the Gulbenkian Foundation (Fundação Calouste Gulbenkian) and the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement 101001332 to R.H.). Funded by the European Union. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This work was also supported by a European Molecular Biology Organization (EMBO) installation grant (EMBO-2020-IG-4734 to R.H.), an EMBO postdoctoral fellowship (EMBO ALTF 174-2022 to E.G.-d.-M.), a Chan Zuckerberg Initiative Visual Proteomics Grant (vpi-0000000044 with to R.H.). R.H. also acknowledges the support of LS4FUTURE Associated Laboratory (LA/P/0087/2020). This work is partially supported by grant GIU19/027 (to I.A.-C.) funded by the University of the Basque Country (UPV/EHU), grant PID2021-126701OB-I00 (to I.A.-C.) funded by the Ministerio de Ciencia, Innovación y Universidades, MICIU/AEI/10.13039/501100011033, and “ERDF A way of making Europe” (to I.A.-C.). This study was also supported by the Academy of Finland (338537 to G.J.), the Sigrid Juselius Foundation (to G.J.), the Cancer Society of Finland (Syöpäjärjestöt; to G.J.), and Solutions for Health strategic funding to Åbo Akademi University (to G.J.). This research was supported by the InFLAMES Flagship Programme of the Academy of Finland (decision number 337531).

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Authors and Affiliations




I.H.-C., G.J., R.H. and E.G.-d.-M. conceived, designed and wrote the source code of the project with contributions from all co-authors; I.H.-C., J.P.W., M.G.F., C.R., A.S., Y.S., G.J., R.H. and E.G.-d.-M. tested the platform; I.H.-C., J.P.W., M.G.F., G.J., R.H. and E.G.-d.-M. wrote the user documentation; I.H.-C., G.J., R.H. and E.G.-d.-M. wrote the paper with input from all co-authors. F.J. ( serves as a contact for the consortium.

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Correspondence to Guillaume Jacquemet, Ricardo Henriques or Estibaliz Gómez-de-Mariscal.

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The authors declare no competing interests.

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Nature Methods thanks Eugene Katrukha, Nils Körber and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Hidalgo-Cenalmor, I., Pylvänäinen, J.W., G. Ferreira, M. et al. DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible. Nat Methods 21, 925–927 (2024).

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