EMBL Course: Deep Learning for Image Analysis

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.
 

Course Overview

This is a blended learning course on Deep Learning for Image Analysis.

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 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

Organisers

Anna Kreshuk, EMBL Heidelberg, Germany
Constanin Pape, EMBL Heidelberg, Germany
Pejman Rasti, Universit of Angers and Esaip, France
David Rousseau, University of Angers, France
Martin Weigert, Max Planck Institute of Molecular Cell Biology and Genetics, Germany
 

OCT 10: Registration Deadline Register now

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