Microscopie image analysis on Bio Image Archive
- Interact programmatically with the BioImage Archive and other data resources
- Apply pre-built machine learning models to image data
- Train and retrain machine learning models on image data
- Utilise machine learning approaches for object detection, image segmentation and de-noising
- Generate quantitative conclusions from images
This course is aimed at scientists working with bioimage data across the life sciences. It is suitable for those involved in creating bioimages or taking their first steps in analysis. The content would also be suitable for those wanting to learn more about the BioImage Archive and gain experience with machine learning approaches for image analysis. The programme will be of particular interest to bio-image analysts with questions relating to the use of ‘big data’ and using the wealth of publically available data curated in the BioImageArchive.
The course should be accessible to members of the bioimaging community and does not require prior experience with machine learning methods or use of the BioImage Archive is necessary, but applicants are encouraged to explore the resources below before starting their application. Applicants should be comfortable with basic programming tasks and have experience working with Python.
- Nature: BioImage Archive: A call for public archives for biological image data
- biorxiv: ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy
- Nucleic Acids Research: The BioStudies database—one stop shop for all data supporting a life sciences study
- Nature Methods: EMPIAR: a public archive for raw electron microscopy image data
- Nature: Image Data Resource: a bioimage data integration and publication platform