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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Vydané v:bioRxiv
Hlavní autori: 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
Médium: Paper
Jazyk:English
Vydavateľské údaje: Cold Spring Harbor Laboratory 08.06.2022
Vydanie:1.1
Predmet:
Bioinformatics
ISSN:2692-8205
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Popis
Shrnutí: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.
Bibliografia:Competing Interest Statement: The authors have declared no competing interest.
ISSN:2692-8205
DOI:10.1101/2022.06.07.495102