RT Journal Article
SR Electronic
T1 BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.07.495102
DO 10.1101/2022.06.07.495102
A1 Wei Ouyang
A1 Fynn Beuttenmueller
A1 Estibaliz Gómez-de-Mariscal
A1 Constantin Pape
A1 Tom Burke
A1 Carlos Garcia-López-de-Haro
A1 Craig Russell
A1 Lucía Moya-Sans
A1 Cristina de-la-Torre-Gutiérrez
A1 Deborah Schmidt
A1 Dominik Kutra
A1 Maksim Novikov
A1 Martin Weigert
A1 Uwe Schmidt
A1 Peter Bankhead
A1 Guillaume Jacquemet
A1 Daniel Sage
A1 Ricardo Henriques
A1 Arrate Muñoz-Barrutia
A1 Emma Lundberg
A1 Florian Jug
A1 Anna Kreshuk
YR 2022
UL http://biorxiv.org/content/early/2022/06/08/2022.06.07.495102.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.