Deep Learning for Image Analysis, 8-12 February 2021, EMBL Virtual course
Neural networks have been successfully applied to various medical and biological imaging modalities including PALM/STORM, light sheet fluorescence microscopy, high-throughput microscopy, electron microscopy, X-ray tomography. However, they require observation-outcome-pairs for training.
This is a blended learning course on Deep Learning for Image Analysis, consisting of pre-course online sessions in December 2020 and/or January 2021
with associated hands-on exercises and a week-long virtual course in February 2021.
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.
For additonal information please check the course pages.
Application deadline 16 November 2020