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.
High content single cell phenotypic profiling, Deep learning in microscopy, Public data repositories, data harmonization, integration and fusion, Data-modeling of live cell imaging
Assaf Zaritsky
No prior biological knowledge is required; all background will be covered in the lectures. Some background in mathematics and programming is required. Prior knowledge in machine learning and/or computer vision is highly recommended, but not necessary.
Early Careers: Ideally suited. Learn how to make reproducible automated bioimage analysis workflows even without programming knowledge
Bioimage Analysts / Facility Staff: Very useful for teaching purposes and if you need to quickly deliver modifiable, reusable workflows to non-programming user
