2023/11/19 by Iván Hidalgo-Cenalmor, Joanna W. Pylvänäinen, Mariana Ferreira +5 · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · Decision Sciences · #Cell Image Analysis Techniques #Scientific Computing and Data Management #Single-cell and spatial transcriptomics
paper · pdf · doi:10.1101/2023.11.19.567606
openalex publication_date 2023/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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.