e-learning
Execute a BiaPy workflow in Galaxy
Abstract
The application of supervised and unsupervised Deep Learning (DL) methods in bioimage analysis have been constantly increasing in biomedical research in the last decades.
About This Material
This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.
Questions this will address
- What is BiaPy and how does it streamline deep learning workflows for bioimage analysis?
- How can we make Deep Learning (DL) models accessible to a broader audience?
- How can I execute a BiaPy pipeline directly within the Galaxy platform?
- How do I utilize pre-trained models from the BioImage.IO repository to perform inference on image data?
Learning Objectives
- Learn to configure and run a BiaPy workflow by editing a YAML file to define hardware settings, data paths, and model selection.
- Execute an inference workflow in Galaxy using two different pre-trained models sourced from BioImage.IO.
Licence: Creative Commons Attribution 4.0 International
Keywords: 3D image, Conversion, Deep learning, Image annotation, Image segmentation, Imaging, Overlay, Volume rendering
Target audience: Students
Resource type: e-learning
Version: 1
Status: Active
Prerequisites:
- FAIR Bioimage Metadata
- Introduction to Galaxy Analyses
- REMBI - Recommended Metadata for Biological Images – metadata guidelines for bioimaging data
Learning objectives:
- Learn to configure and run a BiaPy workflow by editing a YAML file to define hardware settings, data paths, and model selection.
- Execute an inference workflow in Galaxy using two different pre-trained models sourced from BioImage.IO.
Date modified: 2026-02-11
Date published: 2026-02-11
Contributors: Beatriz Serrano-Solano, Leonid Kostrykin
Scientific topics: Imaging
External resources:
Activity log
