Deep learning for image analysis – Course and Conference Office

EMBL Course

Deep learning for image analysis


Registration is not yet open for this event. If you are interested in receiving more information please register your interest.

Course overview

The advent of deep learning has brought a revolution in the field of computer vision, including most tasks and research questions concerned with microscopy image analysis. Neural networks have been successfully used for image restoration, classification and segmentation, for the detection of objects and characterisation of their morphology, for high-throughput imaging and large-scale processing in 3D. Despite these advances, training and deployment of such neural networks remains difficult for practitioners of image analysis. The aim of our course is to close this gap and teach the participants – in the most hands-on way possible – to apply deep learning-based methods to their own data and image analysis problems.

Participants are invited to bring their own data sets that can be used in the practical sessions.


This course is aimed at both core facility staff and research scientists.

Prerequisites for this workshop are programming skills in Python and ideally Tensorflow, Keras or Pytorch as well as basic knowledge of machine learning theory.

What past participants say about the course

“I attended the Deep Learning course and I couldn’t be happier with that decision. The venue, the instructors, the content and the organization was excellent. Social interactions were on par, definitely a great experience.” – Alberto Díez, CEMIR – NTNU, Norway

“I recently attended the ‘Deep learning for Image analysis’ course at EMBL and I cannot
express enough how immensely useful, informative and enjoyable it was. The course not only gives the participants a complete overview and understanding of existing tools and techniques through lectures given by experts in the field, but it also provides them with the opportunity to apply these techniques on their own data and scientific problem. I really appreciated the overall hands-on approach and I am extremely grateful for the trainers help throughout the practicals.”
– Camille Lambert, EPFL, Switzerland


Sponsorship opportunities

We offer a variety of event sponsoring possibilities, with the flexibility to select a set sponsorship package or combine individual sponsorship options to suit your event budget. Discounts are available for companies sponsoring multiple events at EMBL Heidelberg. View other events, or contact sponsorship@embl.de for further information.

If you are interested in becoming a media partner of this event, please visit our media partnerships webpage.


EMBL wishes to warn sponsors of EMBL conferences and courses of fraudulent schemes purporting to offer sponsorship opportunities on behalf of EMBL or affiliated with EMBL officials. One current scam campaign of which we are aware is conducted using the name ‘Judy Eastman’ (judy@gopcontact.a2hosted.com) and entails approaches to sponsors offering sponsorship opportunities on EMBL’s behalf. Please be kindly advised that all relevant communication regarding sponsorship of EMBL conferences, symposia and courses is handled by EMBL directly and is sent from an official EMBL account. EMBL does not work with any external providers on sponsorship acquisition.

Please also note that:

  • EMBL never provides attendee lists for purchase. Any offers of such are fraudulent.
  • EMBL will never call or email you to ask for your credit card details or to request a payment.
  • All payments are on invoice.

Suspicious communications purportedly from, for or on behalf of EMBL should be reported to EMBL at the following email address sponsorship@embl.de.

Date: 17 - 21 Feb 2025

Location: EMBL Heidelberg

Venue: EMBL Advanced Training Centre

Contact: EMBL Events

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