FRIDAY 26 APRIL 2019 | 08:45-17:30 

  • Course directors: B. Heijmen (NL) and D. Verellen (BE)
     

Course aim

To provide basic knowledge on machine learning and its application. On the one hand there is a focus on a true understanding of the methodology, on the other hand practical approaches for getting started with machine learning will be discussed, e.g. using open source software. The course aims at enabling medical physicists to understand and critically evaluate clinical applications from a user point of view. For investigators and developers the course may be helpful in getting started in the field. The course assumes that the participants have no knowledge on the subject.
 

Learning objectives

After following this course the participants will be able to:

  • understand the fundamental basics of machine learning,describe and explain the most common algorithms, methods and approaches related to machine learning,
  • understand concepts such as artificial intelligence, machine learning, deep learning, supervised and unsupervised learning,
  • understand for what type of problems machine learning is most suited and for what problems other approaches/algorithms are better,
  • identify advantages and disadvantages of different approaches of machine learning in relation to applications in radiation oncology,
  • explore existing tutorials and sources for open source software to start up a program.
     

Target audience

Medical physicists with no or little prior knowledge that want to understand the basics of machine learning in order to implement and use existing applications safely in a clinical workflow. The course also provides a starting point for those physicists that are interested in learning how to develop their own applications.


Topics

  • Basic introduction on machine learning
  • Overview of some common approaches (from basic notions to some practical examples)
  • Tips and tricks to get started from colleagues that recently went through the process
     

Programme
 

Time slot

Title

Teacher

08:45-09.00

Introduction to the course

Ben Heijmen (NL) / Dirk Verellen (BE)

09:00-09:45

Introduction to artificial intelligence in radiotherapy

Issam El Naqa (USA)

09:45-10:30

Overview of machine learning algorithms and practical considerations

Issam El Naqa (USA)

10:30-11:00

COFFEE BREAK

11:00-11:45

Conventional machine learning techniques

Sarah Gulliford (UK)

11:45-12:30

Deep learning techniques

Frederik Maes (BE)

12:30-14:00

LUNCH

14:00-14:45

Deep learning for head and neck segmentation

Frederik Maes (BE)

14:45-15:30

Current and future clinical applications of machine learning in radiotherapy

Sarah Gulliford (UK)

15:30-16:00

COFFEE BREAK

16:00-16:45

Commissioning and QA of Machine Learning Algorithms for clinical use

Issam Al Naqa

16:45-17:30

How to start a machine learning project – a practical example

Jennifer Dhont (BE)

17:30

Closure of pre-meeting

Ben Heijmen (NL) / Dirk Verellen (BE)