HT Course – Deep Learning for Microscopy Image Analysis


Location: virtual
Date & Time: 18-22 July 2022, h 9:00 – 22:00 (Monday to Thursday), 9:00 – 19:00 (Friday)
Registration closed
AIM OF THE COURSE
The goal of this course is to familiarize researchers working in life sciences with state-of-the-art deep learning techniques for microscopy image analysis, with a focus on image restoration and image segmentation. Our aim is to introduce tools and frameworks that will facilitate independent application of the learned material after the course.
The following topics will be covered extensively during lectures, exercises, and project work:
- image denoising and restoration (fully supervised, self-supervised and unsupervised machine learning),
- image segmentation (pixel classification, instance segmentation, shallow and deep approaches),
- failure cases and limitations.
The course will be organised in two phases: (1) First three days with lectures and exercises to introduce participants to the basic concepts of deep learning and familiarize them with the methods and tools. (2) Last two days with hands-on projects, where students will work together and with trainers to apply the newly acquired skills to their own datasets.
Participants will leave the course with an appreciation for the power and limitations of deep learning, as well as with helpful insights into the underlying theory of machine learning techniques and the most prevalent tools for design and training of neural networks.
TARGET AUDIENCE
Up to 20 participants who are expected to have coding/scripting skills and some familiarity with Python programming, with no necessary prior experience with machine learning or deep learning techniques. Participants are strongly encouraged to bring their own microscopy datasets to work on during the project phase.
COURSE REQUIREMENTS
Participants will work on virtual machines and need access to a computer with high-speed internet connection. The course requires a Zoom installation.
Contact: training@fht.org
LECTURERS
- Anna Kreshuk, EMBL, DE
- Alexander Krull, University of Birmingham, UK
- Virginie Uhlmann, EMBL-EBI, UK
- Laura Waller, UC Berkeley, US
HT TRAINERS
-
Florian Jug
Research Group Leader -
Christopher Schmied
Bioimage Computing and AI Researcher -
Joran Deschamps
Bioimage Computing and AI Researcher -
Damian Edward Dalle Nogare
Bioimage Analyst and Research Software Engineer
SCIENTIFIC ORGANISER
-
Florian Jug
Research Group Leader