BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis

Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning...

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Hauptverfasser: Ouyang, Wei, Beuttenmueller, Fynn, Gómez-de-Mariscal, Estibaliz, Pape, Constantin, Burke, Tom, Garcia-López-de-Haro, Carlos, Russell, Craig, Moya-Sans, Lucía, de-la-Torre-Gutiérrez, Cristina, Schmidt, Deborah, Kutra, Dominik, Novikov, Maksim, Weigert, Martin, Schmidt, Uwe, Bankhead, Peter, Jacquemet, Guillaume, Sage, Daniel, Henriques, Ricardo, Muñoz-Barrutia, Arrate, Lundberg, Emma, Jug, Florian, Kreshuk, Anna
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Veröffentlicht: Cold Spring Harbor Laboratory 08.06.2022
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Bioinformatics
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Abstract Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning by providing user-friendly interfaces to analyze new data with pre-trained or fine-tuned models. Still, few of the existing pre-trained models are interoperable between these tools, critically restricting a model’s overall utility and the possibility of validating and reproducing scientific analyses. Here, we present the BioImage Model Zoo (https://bioimage.io): a community-driven, fully open resource where standardized pre-trained models can be shared, explored, tested, and downloaded for further adaptation or direct deployment in multiple end user-facing tools (e.g., ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic, CSBDeep). To enable everyone to contribute and consume the Zoo resources, we provide a model standard to enable cross-compatibility, a rich list of example models and practical use-cases, developer tools, documentation, and the accompanying infrastructure for model upload, download and testing. Our contribution aims to lay the groundwork to make deep learning methods for microscopy imaging findable, accessible, interoperable, and reusable (FAIR) across software tools and platforms.
AbstractList Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning by providing user-friendly interfaces to analyze new data with pre-trained or fine-tuned models. Still, few of the existing pre-trained models are interoperable between these tools, critically restricting a model’s overall utility and the possibility of validating and reproducing scientific analyses. Here, we present the BioImage Model Zoo (https://bioimage.io): a community-driven, fully open resource where standardized pre-trained models can be shared, explored, tested, and downloaded for further adaptation or direct deployment in multiple end user-facing tools (e.g., ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic, CSBDeep). To enable everyone to contribute and consume the Zoo resources, we provide a model standard to enable cross-compatibility, a rich list of example models and practical use-cases, developer tools, documentation, and the accompanying infrastructure for model upload, download and testing. Our contribution aims to lay the groundwork to make deep learning methods for microscopy imaging findable, accessible, interoperable, and reusable (FAIR) across software tools and platforms.
Author Henriques, Ricardo
de-la-Torre-Gutiérrez, Cristina
Kreshuk, Anna
Pape, Constantin
Sage, Daniel
Beuttenmueller, Fynn
Lundberg, Emma
Weigert, Martin
Bankhead, Peter
Burke, Tom
Kutra, Dominik
Muñoz-Barrutia, Arrate
Schmidt, Uwe
Moya-Sans, Lucía
Schmidt, Deborah
Novikov, Maksim
Jug, Florian
Gómez-de-Mariscal, Estibaliz
Ouyang, Wei
Russell, Craig
Jacquemet, Guillaume
Garcia-López-de-Haro, Carlos
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  fullname: Ouyang, Wei
  organization: Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology
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  organization: Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg University
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  fullname: Gómez-de-Mariscal, Estibaliz
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  organization: Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)
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  organization: European Molecular Biology Laboratory, European Bioinformatics Institute
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  fullname: de-la-Torre-Gutiérrez, Cristina
  organization: Bioengineering and Aerospace Engineering Department, Universidad Carlos III de Madrid
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  givenname: Deborah
  surname: Schmidt
  fullname: Schmidt, Deborah
  organization: Helmholtz Imaging, Max Delbrück Center for Molecular Medicine in the Helmholtz Association
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  givenname: Dominik
  surname: Kutra
  fullname: Kutra, Dominik
  organization: Cell Biology and Biophysics Unit, European Molecular Biology Laboratory
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  surname: Novikov
  fullname: Novikov, Maksim
  organization: Cell Biology and Biophysics Unit, European Molecular Biology Laboratory
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  surname: Weigert
  fullname: Weigert, Martin
  organization: Institute of Bioengineering, School of Life Sciences, EPFL
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  givenname: Uwe
  surname: Schmidt
  fullname: Schmidt, Uwe
  organization: Independent researcher
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  givenname: Peter
  orcidid: 0000-0003-4851-8813
  surname: Bankhead
  fullname: Bankhead, Peter
  organization: Edinburgh Pathology, Institute of Genetics and Cancer, University of Edinburgh
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  givenname: Guillaume
  orcidid: 0000-0002-9286-920X
  surname: Jacquemet
  fullname: Jacquemet, Guillaume
  organization: Turku Bioimaging, University of Turku and Åbo Akademi University
– sequence: 17
  givenname: Daniel
  surname: Sage
  fullname: Sage, Daniel
  organization: Biomedical Imaging Group and EPFL Center for Imaging, Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne
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  givenname: Ricardo
  surname: Henriques
  fullname: Henriques, Ricardo
  organization: MRC-Laboratory for Molecular Cell Biology, University College London
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  givenname: Arrate
  orcidid: 0000-0002-1573-1661
  surname: Muñoz-Barrutia
  fullname: Muñoz-Barrutia, Arrate
  organization: Instituto de Investigación Sanitaria Gregorio Marañón
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  givenname: Emma
  surname: Lundberg
  fullname: Lundberg, Emma
  organization: Chan Zuckerberg Biohub
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  givenname: Florian
  surname: Jug
  fullname: Jug, Florian
  organization: Fondazione Human Technopole
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  givenname: Anna
  orcidid: 0000-0003-1334-6388
  surname: Kreshuk
  fullname: Kreshuk, Anna
  email: anna.kreshuk@embl.de
  organization: Cell Biology and Biophysics Unit, European Molecular Biology Laboratory
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Notes Competing Interest Statement: The authors have declared no competing interest.
ORCID 0000-0002-9286-920X
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Snippet Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based...
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SubjectTerms Bioinformatics
Title BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis
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