DL4MicEverywhere: Deep learning for microscopy made flexible, shareable, and reproducible
Published in Nature Methods, 2023
Recommended citation: Hidalgo-Cenalmor, I., Pylvänäinen, J.W., Ferreira, M.G., Russell, C.T., Arganda-Carreras, I., AI4Life Consortium, Jacquemet, G., Henriques, R., & Gómez-de-Mariscal, E. (2024). DL4MicEverywhere: Deep learning for microscopy made flexible, shareable, and reproducible. *Nature Methods*, 21, 925–927. https://doi.org/10.1038/s41592-024-02295-6 https://www.nature.com/articles/s41592-024-02295-6
DL4MicEverywhere: Deep Learning for Microscopy Made Flexible, Shareable, and Reproducible
Journal: Nature Methods (Vol. 21, June 2024, Pages 925–927)
Preprint: bioRxiv version
Code Repository: github.com/HenriquesLab/DL4MicEverywhere
🚀 Summary
DL4MicEverywhere expands upon ZeroCostDL4Mic, making deep learning methods for microscopy:
- More flexible: usable across diverse environments via Dockerized Jupyter Notebooks
- More shareable: enabling collaboration with reproducible containers
- More reproducible: thanks to standardized interactive pipelines
🔬 Why it matters
Many researchers lack the infrastructure or skills to deploy advanced deep learning models. DL4MicEverywhere solves this by bundling tools and code inside portable environments, allowing scientists to run training and inference with minimal configuration.
The platform:
- Doubles the number of techniques originally offered by ZeroCostDL4Mic
- Encourages community contributions through automated CI pipelines
- Enables faster onboarding and experimentation in bioimage analysis
This democratizes AI access for experimentalists, educators, and developers alike.
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