Artificial Intelligence 4 Imaging - Radiomics, Deep Learning, Synthetic Data and Distributed Learning - a hands-on course

Maastricht, The Netherlands

[ Early deadline: 1 September 2019 ]

This course on Artificial Intelligence for Imaging is a unique opportunity to join a community of leading edgePastedGraphic-1.jpg practitioners in the field of Quantitative Medical Imaging. During this 4-days immersive course, you will be able to attend lectures and workshops from world-class experts in Radiomics, Deep Learning, Synthetic Data, and Distributed Learning.  You can also bring your own curated dataset with you for the hackathon (labelled, sorted by outcome, open source or fully anonymised, and cleared by ethics).  If requested ahead of time, we will perform “data matching” for attendees to facilitate external cross validation. There will be ample opportunity to network with faculty members, other participants and companies.

  • Philippe Lambin, Maastricht University, The Netherlands (Course Director)
  • Arthur Jochems, Maastricht University, The Netherlands (Organiser)
  • Henry Woodruff, Maastricht University, The Netherlands (Organiser)
  • Samir Barakat, Maastricht University, The Netherlands
  • Joe Deasy, Memorial Sloan Kettering Cancer Center, USA
  • Michel Dumontier, Maastricht University, The Netherlands
  • Olivier Gevaert, Stanford University, USA
  • Robert J. Gillies, Moffitt Cancer Center, Tampa, USA
  • Mathieu Hatt, LaTIM INSERM, France
  • Andrew Maidment, University of Pennsylvania School of Medicine, Philadelphia, USA
  • Bjoern Menze, TU München, Germany
  • Olivier Morin, UCSF & Principal Investigator of the Morin QI Lab, California, USA (Organiser)
  • Martin Vallières, McGill University, Canada
  • Sean Walsh, Maastricht University, The Netherlands

TARGET AUDIENCE

  • clinicians in medical imaging (e.g. radiologists, oncologists, neurologists, cardiologists, ophthalmologists, dermatologists, ENT surgeons)
  • medical physicists with an interest in research
  • medical imaging researchers
  • computer scientists with an interest in medical imaging
  • academics researching quantitative imaging

LEARNING OBJECTIVES

Regarding Radiomics, Deep Learning, Synthetic Data (TECHNICAL TRACT), and Distributed Learning, after this course you will be able to:

  • explain the fundamentals
  • critically evaluate the literature
  • draw up an implementation plan for the clinic (CLINICAL TRACT)
  • understand the pitfalls associated with quantitative imaging
  • provide advice in designing Quantitative Image Analysis Experiments
  • make data FAIR (Findable, Accessible, Interoperable, Reusable)
  • comply with regulation and privacy laws (DGPR)

Practicalities

  • Max 100 participants 
  • The course will be divided into lectures during the morning and hands on assignments in the afternoon. Parts of the course will be split into clinical and technical tracts, depending on your level of expertise. Participants of the hackathon are encouraged to come with their data and we will organize (if possible) matching data for validation from other participants on the course.

Maastricht School of Management

Endepolsdomein 150, 6229 EP Maastricht

The Netherlands

www.msm.nl

Rooms can be booked in NH Maastricht at the rate of EUR 99,00 for a standard twin room for single use including breakfast (excl. EUR 4,77 EUR city taks per person per night)

Reservation deadline: 30 September 2019

For bookings and questions, please contact the hotel directly – Nathalie Collard – n.collard@nh-hotels.com (Subject of the e-mail: Radiomics & Deep learning.)