HT Deep Learning for Microscopy Image Analysis 2023

The goal of the course is to familiarise 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 familiarise 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.

GO TO THE COURSE WEBPAGE

Contact: training@fht.org