Technology
DL4MicEverywhere
GitHub:
- HenriquesLab/DL4MicEverywhere
Publication: Hidalgo-Cenalmor et al. Nature Methods 2024
- 8
DL4MicEverywhere is an open-source platform that aims to make deep learning more accessible for bioimage analysis. It builds on ZeroCostDL4Mic, extending it so that models can be trained and run across a wider range of computing environments.
The key goal of DL4MicEverywhere is to democratize deep learning for microscopy image analysis by tackling some of the main barriers to adoption. Many researchers have no access to training datasets large enough, or annotated well enough, to produce an accurate model. Training such networks also needs GPUs, which are expensive. Few biomedical researchers have experience designing, training and deploying them.
DL4MicEverywhere packages deep learning methods for bioimage analysis into Docker containers. Training and inference then run on laptops, workstations, clusters and cloud platforms with no dependencies to install. Pre-trained models are shared so that work can start on real data straight away.
A key emphasis in DL4MicEverywhere is on transparency, reproducibility and convenience. Self-contained Docker images behave the same on every operating system, and the documentation includes usage tutorials. Because the code is open, the methods can be reviewed, improved and extended by anyone. Researchers are encouraged to containerise their own models and contribute them back.
Publications featuring DL4MicEverywhere
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Packaging Jupyter notebooks as installable desktop apps using LabConstrictor Iván Hidalgo-Cenalmor, Marcela Xiomara Rivera Pineda, Bruno M Saraiva, Ricardo Henriques, Guillaume Jacquemet Preprint published in arXiv, March 2026 Technologies: CARE (), CellTracksColab (), DL4MicEverywhere (), mAIcrobe () and ZeroCostDL4Mic () Funded by: Chan Zuckerberg Initiative (CZI), The Kavli Foundation, and The Wellcome Trust, EMBO, ERC, H2022 and La Caixa Foundation DOI: 10.48550/arXiv.2603.10704 |
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EZInput: A Cross-Environment Python Library for Easy UI Generation in Scientific Computing Bruno M Saraiva, Iván Hidalgo-Cenalmor, António D Brito, Damián Martínez, Tayla Shakespeare, Guillaume Jacquemet, Ricardo Henriques Preprint published in arXiv, January 2026 Technologies: DL4MicEverywhere (), mAIcrobe (), NanoJ-eSRRF (), NanoPyx () and ZeroCostDL4Mic () Funded by: Chan Zuckerberg Initiative (CZI), The Kavli Foundation, and The Wellcome Trust, CZI, EMBO, ERC, H2021 and H2022 DOI: 10.48550/arXiv.2601.08859 |
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Rxiv-Maker: An Automated Template Engine for Streamlined Scientific Publications Bruno M Saraiva, Guillaume Jaquemet, Ricardo Henriques Preprint published in arXiv, June 2025 Technologies: DL4MicEverywhere () Funded by: Chan Zuckerberg Initiative (CZI), The Kavli Foundation, and The Wellcome Trust, CZI, EMBO, ERC, H2021 and H2022 DOI: 10.48550/arXiv.2508.00836 |
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ReScale4DL: Balancing Pixel and Contextual Information for Enhanced Bioimage Segmentation Mariana G. Ferreira, Bruno M. Saraiva, António D. Brito, Mariana G. Pinho, Ricardo Henriques, Estibaliz Gómez-de-Mariscal Preprint published in bioRxiv, April 2025 Technologies: BioImage Model Zoo (), DeepBacs (), DL4MicEverywhere (), NanoPyx (), Rescale4DL () and ZeroCostDL4Mic () Funded by: CZI, EMBO, ERC, H2021 and H2022 DOI: 10.1101/2025.04.09.647871 |
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DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible Iván Hidalgo-Cenalmor, Joanna W. Pylvänäinen, Mariana G. Ferreira, Craig T. Russell, Alon Saguy, Ignacio Arganda-Carreras, Yoav Shechtman, Guillaume Jacquemet, Ricardo Henriques, Estibaliz Gómez-de-Mariscal Paper published in Nature Methods, May 2024 Technologies: BioImage Model Zoo (), DL4MicEverywhere () and ZeroCostDL4Mic () Funded by: EMBO, ERC, H2021 and H2022 DOI: 10.1038/s41592-024-02295-6 |
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Funding contributing to DL4MicEverywhere
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VirusAwareScopes: Machine Learning-Driven Adaptive Microscopy for Long-Term Viral Infection Studies Ricardo Henriques Alias: VirusAwareScopes Funded by: La Caixa Foundation - Health Research Duration: November 2025 - October 2028 Publications: 2 |
