This virtual course will show how public bioimaging data resources, centred around the BioImage Archive, enable and enhance machine learning based image analysis. The content will explore a variety of data types including electron and light microscopy and miscellaneous or multi-modal imaging data at the cell and tissue scale. Participants will cover contemporary biological image analysis with an emphasis on machine learning methods, as well as how to access and use images from databases.
This course will be a virtual event delivered via a mixture of live-streamed sessions, pre-recorded lectures, and tutorials with live support. We will be using Zoom to run the live sessions (all fully password protected with automated English closed captioning and transcription) with support and both scientific and social networking opportunities provided by Slack and other methods, taking different time zones into account.
Who is this course for?
This course is aimed at scientists working with biomage data across the life sciences. It is suitable for those involved in creating bioimages or taking their first steps in analysis. The content is also 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 bioimage analysts with questions relating to the use of ‘big data’ and using the wealth of publicly available data curated in the BioImage Archive.
The course is accessible to members of the bioimaging community and does not require prior experience with machine learning methods or use of the BioImage Archive. Applicants should be comfortable with basic programming tasks and have experience working with Python.
Learning outcomes
After the course you should be able to:
- Interact programmatically with the BioImage Archive and other data resources
-Interact with data formats for biological imaging data, particularly cloud-optimised formats
-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