Deep Learning for Image Analysis

Overview
Speakers and Trainers
Programme

Course Overview

This is a blended learning course on Deep Learning for Image Analysis, consisting of 3 online sessions with associated hands-on exercises and a week-long onsite session at EMBL Heidelberg.

Audience

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 on-site part of the 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. Ideally, you will be provide annotated images for network training during the course.

Learning Outcomes

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