EMBL Courses and Conferences during the Coronavirus pandemic
With the onsite programme paused, many of our events are now being offered in virtual formats.
Registration is open as usual for many events, with back-up plans in place to move further courses and conferences online as necessary. Registration fees for any events affected by the COVID-19 disruption are fully refundable.
More information for participants of events at EMBL Heidelberg can be found here.
EMBL is committed to sharing research advances and sustaining scientific interaction throughout the coronavirus pandemic. We are delighted to announce that the course is going virtual and invite you to join us online.
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.
This is a blended learning course. We will begin with 3 pre-course sessions (on Friday 18 Dec 2020, Friday 15 January 2021 and Friday 29 January 2021) which will bring everyone to a good starting position for a week-long intensive virtual course in February 2021. The 3 pre-course sessions will have associated homework. The week-long course in February 2021 will include a few talks, but will mostly be devoted to hands-on work on real data, in small groups of 3-4 participants.
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. Participants should provide an outline of one image analysis task they would like to work on during the course. Ideally, you will provide annotated images for network training during the course.
After this course you should be able to:
- Understand the fundamentals of machine learning methods suitable for image analysis
- Advise users/colleagues in strategies to obtain ground truth
- Train and use a CNN for a bioimage analysis task studied in the course
- Perform simple quality control on the results