DL4MicEverywhere is a platform that offers researchers an easy-to-use gateway to cutting-edge deep learning techniques for bioimage analysis. It features interactive Jupyter notebooks with user-friendly graphical interfaces that require no coding skills. The platform utilizes Docker containers to ensure portability and reproducibility, guaranteeing smooth operation across various computing environments.
DL4MicEverywhere extends the capabilities of ZeroCostDL4Mic by allowing the execution of notebooks either locally on personal devices like laptops or remotely on diverse computing platforms, including workstations, high-performance computing (HPC), and cloud-based systems. It currently incorporates numerous pre-existing ZeroCostDL4Mic notebooks for tasks such as segmentation, reconstruction, and image translation.
DL4MicEverywhere is designed to make deep learning more accessible, transparent, and participatory. This enables broader adoption of advanced techniques while enhancing reliability and customization.
Windows_launch
for Windows operating systems). If this is the first time you run DL4MicEverywhere, we recommend you to follow the provided steps.With Docker, all dependencies are neatly bundled. Just launch and access deep learning workflows through an intuitive interface!
Refer to the Step-by-step "How to" guide and Requirements Installation Guidelines for further details.
Reproduce the demo in the video with the U-Net (2D) multilabel
notebook and Bacillus subtilis segmentation data from DeepBacs. Note that run time will vary from minutes to hours depending on the GPU availability and computing resources.
DL4MicEverywhere rely on the following external software that is automatically installed when launching the tool.
If GPU acceleration is desired, the following needs to be installed:
We welcome contributions! Please check out the contributing guidelines to get started.
Don't hesitate to reach out if you need any clarification!
We extend our gratitude to the ZeroCostDL4Mic contributors for their work on the original notebooks. We also thank the AI4Life consortium for their support and continuous feedback.
Iván Hidalgo-Cenalmor, Joanna W. Pylvänäinen, Mariana G. Ferreira, Craig T. Russell, Alon Saguy, Ignacio Arganda-Carreras, Yoav Shechtman, AI4Life Horizon Europe Program Consortium, Guillaume Jacquemet, Ricardo Henriques & Estibaliz Gómez-de-Mariscal. DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible. Nat Methods 2024 DOI: https://doi.org/10.1038/s41592-024-02295-6
@article{hidalgo2024dl4miceverywhere,
title={DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible},
author={Hidalgo-Cenalmor, Iv{\'a}n and Pylv{\"a}n{\"a}inen, Joanna W and G. Ferreira, Mariana and Russell, Craig T and Saguy, Alon and Arganda-Carreras, Ignacio and Shechtman, Yoav and Jacquemet, Guillaume and Henriques, Ricardo and others},
journal={Nature Methods},
pages={1--3},
year={2024},
publisher={Nature Publishing Group US New York}
}