Resources

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.

Citation

@article{ouyang2022bioimage,
  title={BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis},
  author={Ouyang, Wei and Beuttenmueller, Fynn and G{\'o}mez-de-Mariscal, Estibaliz and Pape, Constantin and Burke, Tom and Garcia-L{\'o}pez-de-Haro, Carlos and Russell, Craig and Moya-Sans, Luc{\'\i}a and de-la-Torre-Guti{\'e}rrez, Cristina and Schmidt, Deborah and others},
  journal={bioRxiv},
  year={2022},
  publisher={Cold Spring Harbor Laboratory}
}