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Machine Learning for Image Analysis

October 29, 2018 - October 31, 2018

(Deadline: June 15, 2018)

Date: Monday 29 – Wednesday 31 October 2018

Venue: EMBL – EMBL- Heidelberg, Meyerhofstraße, 69117,  Heidelberg, Germany

Application opens: Monday May 07 2018

Application deadline: Friday June 15 2018

Participation: Open application with selection

Registration fee: £0

Dates additional information: Up-front online sessions: 2nd, 9th and 16th October 2018 (12:00-14:00 CEST) On-site workshop: 29-31st October 2018 Follow-up online sessions (optional): 9th and 16th November 2018

Course Overview

This is a blended learning course on Machine Learning for Image Analysis, consisting of three online sessions with associated hands-on exercises prior to the workshop, a three day face-to-face workshop at EMBL Heidelberg and two optional online sessions with associated hands-on exercises after the workshop. The course is jointly organised by CORBEL, EMBL, German BioImaging and NEUBIAS (COST Action CA15124).
The course will be a great mix of intensive learning, extensive hands-on and community networking in the field of Machine Learning for Image Analysis.

  • Up-front online sessions: Participants will review the fundamentals of machine learning in three up-front webinars complemented by online tutorials. The webinars will take place on 2nd, 9th and 16th October 2018, 12:00 – 14:00 but a recorded alternative can be provided.
  • On-site workshop: Next, they will apply their knowledge at an on-site workshop (EMBL Heidelberg, October 29-31), in small interactive groups (the workshop has 20 available seats and ~8 trainer/lecturer), to both reference datasets and their own data. Topics to be practiced in these groups include: 2D and 3D segmentation with convolutional neural networks, Content aware image restoration, simulation of ground truth data, labeling strategies and transfer learning. Participants will be asked to pick one group for the practical sessions.
  • Follow-up online sessions (optional): After the on-site workshop, two optional advanced training webinar, complemented by a online tutorials, will be given on 9th and 16th November 2018. These will focus simulation of data, transfer learning and boosting.

Practical Sessions (participants will be asked to choose one during registration):

Detection and Segmentation in microscopic images (Thorsten Falk):

  • Topics: Segmentation with convolutional neural networks, Pattern in bio-medical image data
  • Typical data: Results of multi-labeling high content screens
  • Tools: Python, Keras, Tensorflow

Segmentation in 3D microscopy image stacks (Anna Kreshuk & Constantin Pape):

  • Topics: 3D segmentation (with convolutional neural networks), 2D, 2.5D and 3D networks and their combinations, Pre- and post-processing tricks, Combination of deep and “shallow” learning
  • Typical data: 3D datasets recorded by light or electron microscopy
  • Tools: Python, Keras, PyTorch

Transfer learning and how to use synthetic data for supervised deep learning (David Rousseau & Pejman Rasti):

  • Topic: Basics of transfer learning
  • Typical data: PALM/STORM, 3D cells in spheroid imaged in light sheet fluorescence microscopy and 3D plant roots images in absorption X-ray tomography
  • Tools: Python, Keras, Tensorflow

Content-aware image restoration (Martin Weigert):

  • Topics: Content aware image restoration, Simulation of images for light microscopy
  • Typical data: Pairs of light microscopic images imaged in ideal and suboptimal conditions
  • Tools: Python, Keras, Tensorflow


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 that holds potential to be ideally solved with machine learning. 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.

Learning outcomes

After this course you should be able to:

  • Explain the fundamentals of machine learning methods suitable for image analysis
  • Consult users/colleagues in strategies to obtain ground truth
  • Give advice in training and using a neural-network
  • Perform simple quality control on the results of one selected ML approach


October 29, 2018
October 31, 2018
Event Category:
June 15, 2018




EMBL Heidelberg
EMBL HEIDELBERG Meyerhofstraße 1 69117 Heidelberg, Germany Tel: +49 6221 387-0 Fax: +49 6221 387-8306 + Google Map