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Deep learning for image analysis – Course and Conference Office

EMBL Course

Deep learning for image analysis

Overview

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

The advent of deep learning has brought a revolution in the field of computer vision, including most tasks and research questions concerned with microscopy image analysis. Neural networks have been successfully used for image restoration, classification and segmentation, for the detection of objects and characterisation of their morphology, for high-throughput imaging and large-scale processing in 3D. Despite these advances, training and deployment of such neural networks remains difficult for practitioners of image analysis. The aim of our course is to close this gap and teach the participants – in the most hands-on way possible – to apply deep learning-based methods to their own data and image analysis problems.


This is a blended learning course with practical and theoretical sessions. Participants should provide an outline of one image analysis task they would like to work on during the course.

We will begin with 3 pre-course sessions (on Friday, 3 December 2021, Friday, 17 December 2021 and on Monday, 10 January 2022) which will bring everyone to a good starting position for a week-long intensive virtual course in January 2022. The 3 pre-course sessions will have associated homework. The week-long course in January 2022 will include a few talks, but will mostly be devoted to hands-on work on real data, in small groups of 3-4 participants.

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.

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

Date: 17 - 21 Jan 2022

Location: Virtual


Deadline(s):

Registration: Closed


Organisers:

  • Anna Kreshuk
    EMBL Heidelberg, Germany
    • Constanin Pape
      EMBL Heidelberg, Germany

EMBL Courses and Conferences are kindly supported by our Corporate Partnership Programme

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