Nature Communications (Apr 2021)
Democratising deep learning for microscopy with ZeroCostDL4Mic
- Lucas von Chamier,
- Romain F. Laine,
- Johanna Jukkala,
- Christoph Spahn,
- Daniel Krentzel,
- Elias Nehme,
- Martina Lerche,
- Sara Hernández-Pérez,
- Pieta K. Mattila,
- Eleni Karinou,
- Séamus Holden,
- Ahmet Can Solak,
- Alexander Krull,
- Tim-Oliver Buchholz,
- Martin L. Jones,
- Loïc A. Royer,
- Christophe Leterrier,
- Yoav Shechtman,
- Florian Jug,
- Mike Heilemann,
- Guillaume Jacquemet,
- Ricardo Henriques
Affiliations
- Lucas von Chamier
- MRC-Laboratory for Molecular Cell Biology, University College London
- Romain F. Laine
- MRC-Laboratory for Molecular Cell Biology, University College London
- Johanna Jukkala
- Turku Bioscience Centre, University of Turku and Åbo Akademi University
- Christoph Spahn
- Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt
- Daniel Krentzel
- Electron Microscopy Science Technology Platform, The Francis Crick Institute
- Elias Nehme
- Department of Electrical Engineering, Technion—Israel Institute of Technology
- Martina Lerche
- Turku Bioscience Centre, University of Turku and Åbo Akademi University
- Sara Hernández-Pérez
- Turku Bioscience Centre, University of Turku and Åbo Akademi University
- Pieta K. Mattila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University
- Eleni Karinou
- Centre for Bacterial Cell Biology, Biosciences Institute, Faculty of Medical Sciences, Newcastle University
- Séamus Holden
- Centre for Bacterial Cell Biology, Biosciences Institute, Faculty of Medical Sciences, Newcastle University
- Ahmet Can Solak
- Chan Zuckerberg Biohub
- Alexander Krull
- Center for Systems Biology Dresden (CSBD)
- Tim-Oliver Buchholz
- Center for Systems Biology Dresden (CSBD)
- Martin L. Jones
- Electron Microscopy Science Technology Platform, The Francis Crick Institute
- Loïc A. Royer
- Chan Zuckerberg Biohub
- Christophe Leterrier
- Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto
- Yoav Shechtman
- Department of Biomedical Engineering, Technion—Israel Institute of Technology
- Florian Jug
- Center for Systems Biology Dresden (CSBD)
- Mike Heilemann
- Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt
- Guillaume Jacquemet
- Turku Bioscience Centre, University of Turku and Åbo Akademi University
- Ricardo Henriques
- MRC-Laboratory for Molecular Cell Biology, University College London
- DOI
- https://doi.org/10.1038/s41467-021-22518-0
- Journal volume & issue
-
Vol. 12,
no. 1
pp. 1 – 18
Abstract
Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic.
