DL4MicEverywhere: Deep learning for microscopy made flexible, shareable, and reproducible

DL4MicEverywhere: Deep Learning for Microscopy Made Flexible, Shareable, and Reproducible

Published in: Nature Methods
Authors: Iván Hidalgo-Cenalmor, Joanna W. Pylvänäinen, Mariana G. Ferreira, Craig T. Russell, Ignacio Arganda-Carreras, AI4Life Consortium (myself and other authors from AI4Life), Guillaume Jacquemet, Ricardo Henriques, Estibaliz Gómez-de-Mariscal

Abstract:

Deep learning has revolutionised the analysis of extensive microscopy datasets, yet challenges persist in the widespread adoption of these techniques. Many lack access to training data, computing resources, and expertise to develop complex models. We introduce DL4MicEverywhere, advancing our previous ZeroCostDL4Mic platform, to make deep learning more accessible. DL4MicEverywhere uniquely allows flexible training and deployment across diverse computational environments by encapsulating methods in interactive Jupyter notebooks within Docker containers –a standalone virtualisation of required packages and code to reproduce a computational environment–. This enhances reproducibility and convenience. The platform includes twice as many techniques as originally provided by ZeroCostDL4Mic and enables community contributions via automated build pipelines. DL4MicEverywhere empowers participatory innovation and aims to democratise deep learning for bioimage analysis.

Publication Details:

• Journal: Nature Methods, Volume 21, Issue June 2024, Pages 925–927
• Publication Date: May 17, 2024
• DOI: 10.1038/s41592-024-02295-6

Resources:

• Code Repository: GitHub - HenriquesLab/DL4MicEverywhere
• Preprint: bioRxiv