FAIRy deep-learning for bioImage analysis

In the last decade, deep learning (DL) has profoundly impacted how life sciences researchers conduct their bioimage data analysis. We have witnessed its potential to reach human-level performance in increasing computer vision tasks such as denoising, super-resolution, segmentation, detection, response prediction, or even data-driven microscopy. Yet integrating this breakthrough technology into research pipelines remains a challenge for the scientific community. Training and evaluating DL models requires previous programming expertise, technical knowledge, and a large amount of data, limiting the transfer of this technology to the daily practice of life scientists. Aware of this situation, the bioimage analysis community is increasingly interested in spreading general knowledge about DL and supporting its democratization. These slides introduce some of the pioneering projects that contribute to the landscape of findable, accessible, interoperable, and reusable (FAIR) DL. You will discover the tools developed together with colleagues to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo).