Develop programmatic skills for the analysis of bioimage data.

This course will introduce programmatic approaches used in the analysis of bioimage data via the BioImage Archive. The content will explore a variety of data types including electron microscopy, cell and tissue microscopy, and miscellaneous or multi-modal imaging data. Participants will cover contemporary biological image analysis with an emphasis on machine learning and advanced image analysis. Further instruction will be offered using applications such as ZeroCostDL4Mic, ilastik, the BioImage Model Zoo, and CellProfiler.

The organisers welcome applications from scientists who have specific research questions that look to use publicly available data held in the BioImage Archive and can demonstrate use cases for the resource.

Virtual course

Participants will learn via a mix of pre-recorded lectures, live presentations, and trainer Q&A sessions. Practical experience will be developed through group activities and trainer-led computational exercises. Live sessions will be delivered using Zoom with additional support and asynchronous communication via Slack. 

Pre-recorded material may be provided before the course starts that participants will need to watch, read or work through to gain the most out of the actual training event. In the week before the course, there will be a brief induction session. Computational practicals will run on EMBL-EBI's virtual training infrastructure, meaning participants will not require access to a powerful computer or install complex software on their own machines.

Participants will need to be available between the hours of 09:30-17:30 BST each day of the course. Trainers will be available to assist, answer questions, and provide further explanations during these times.

Who is this course for?

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.

Prerequisite reading:

• 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
• BioModelZoo

What will I learn?

Learning outcomes

After this course you should be able to:

• 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

Course content

During this course you will learn about:

• Data repositories
  • BioImage Archive
  • Image Data Resource
  • EMPIAR
  • BioStudies
• Analysis tools
  • Binder
  • Colaboratory
  • Bioimage Model Zoo
  • ZeroCostDL4Mic
  • CellProfiler

Course funding

This course is funded in part by the Wellcome grant "The Image Data Resource: Making Biological Imaging Data FAIR", award number 212962/Z/18/Z.

Trainers

Please read our application advice before starting your application. In order to be considered for a place on this course, you must do the following:

• Complete the online application form
• Provide a Microsoft Word (.docx) document containing three short paragraphs with a biography, work history and description of your current research interests; each paragraph should be no more than 100 words.
• Provide a letter of support from a supervisor or a senior co-worker explaining why you should be selected for this course.

Please complete the online application form by 23:59 (BST) Friday 14 May.

Incomplete applications will not be considered.

All applicants will be informed of the status of their application (successful, waiting list, rejected) no later than Tuesday 1 May.

Participant Flash Talks

All participants will be asked to give a short presentation about their research work as part of the course. These provide an opportunity to share their research with the other participants and provide a forum for discussion. Further details will be provided following registration.