This virtual course will demonstrate 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.

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 08:00 – 18:00 GMT 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 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 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.

Recommended preparatory reading:

• BioImage Archive: A call for public archives for biological image data
• ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy
• EMPIAR: a public archive for raw electron microscopy image data
• Image Data Resource: a bioimage data integration and publication platform
• BioImage Model Zoo
• OME-Zarr: a cloud-optimised bioimaging file format with international community support
• Introduction to the Unix environment – https://swcarpentry.github.io/shell-novice/index.html
• Introduction to Python – http://swcarpentry.github.io/python-novice-gapminder/ 

What will I learn?

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

Course content

During this course, you will learn about: 

Data repositories

• BioImage Archive
• EMPIAR
• Image Data Resource

Analysis tools

• BioImage Model Zoo
• Colaboratory
• Cellpose
• CellProfiler
• ilastik
• ZeroCostDL4Mic

Analysis tools are subject to minor changes

Trainers

How to apply

Please read our support page before starting your application. To be considered for a place on this course, you must do the following:

• Complete the online application form.
• Ensure you add relevant information to the ‘submission details’ section where you are asked to provide information on your:
  • pre-requisite skills and knowledge
  • current work and course expectations
  • data availability
• Upload one letter of support from your supervisor or a senior colleague detailing reasons why you should be selected for the course.

Please submit all documents during the application process by 23:59 on 7 December 2025. Items marked * in the application are mandatory. Incomplete applications will not be processed. 

All applicants will be informed of the status of their application (successful, waiting list, unsuccessful) by 22 December 2025.  If you have any questions regarding the application process, please contact Omotoke Labiyi.

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. Successful applicants will be required to submit their talks upon registration.

Registration fees 

The registration fee includes: 

• access to the full five-day course programme
• access to a bespoke online course handbook
• secure community space on Slack
• virtual machine to work on that is accessed via the internet
• dedicated online support team
• certificate on completion of the course

* If your company is an EMBL-EBI Industry Programme Member, discounts may be available. This will apply automatically on registration if applicable; alternatively, please contact your event organiser for more information.

Financial assistance
Financial assistance is available to a limited number of participants attending this course.

Registration fee waivers
A limited number of registration fee waivers are offered for this course.

• Application process:
  • Apply for the fee waiver alongside your course application.
  • Provide a brief explanation of why you require the waiver and how attending the course will benefit your career.
• Notification:
  • You will be informed of your waiver status at the same time as the outcome of your course application.
  • If awarded, the registration fee will be waived entirely.

Financial assistance terms and conditions

• Selection process: Recipients of financial assistance will be selected by the scientific organisers during the course application review.
• Selection criteria:
  • Selection is based on scientific merit, your current work or study location, the need for financial support, and the career impact of attending the course.
  • Priority will be given to applicants from Low- and Middle-Income Countries (LMICs).
• Fair consideration: Applying for financial assistance will not impact the outcome of your course application.

Event terms and conditions

• Selection process: Recipients will be selected by the scientific organisers during the course application review.
• Selection criteria:
  • Selection is based on scientific merit, your current work or study location, the need for financial support, and the career impact of attending the course.
  • Priority will be given to applicants from Low- and Middle-Income Countries (LMICs).
• Fair consideration: Applying for financial assistance will not affect your course application outcome.

Course materials

The materials for the previous iterations are available for you to browse. These provide a snapshot of the content that will be covered in the 2026 course.