This course on Big Data for Imaging is a unique opportunity to join a community of leading edge practitioners in the field of Artificial Intelligence for 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 and Distributed Learning. You can also bring your own curated dataset with you (open source or anonymized and cleared by ethics). If requested, 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.
Medical imaging has been the cornerstone for the management of patients for decades, particularly in oncology. Imaging data such as CT, MRI or PET are routinely acquired for every cancer patient in the process of diagnosis, treatment planning, image-guided interventions and response assessment. The use of image analysis in a quantitative way is now considered as one of the most promising techniques to support clinical decisions.
Genomics aims at identifying genes and gene mutation to characterize tumor or normal tissue. Radiomics looks at the phenotypic expression of genes, which results in particular imaging features or signatures able to characterize tumor and normal tissue. Radiomics is the high throughput extraction of large amounts of quantitative image features such as tumor image intensity, (multi-scale) texture, shape and size extracted from standard medical images (e.g., CT, MR, PET) using (semi)automatic software. These features are distilled through machine learning into ‘signatures’ that functions as quantitative imaging biomarkers. Recently the radiomics approach has been enriched by Deep Learning methods. A major challenge for the community is the availability of data in compliance with existing and future privacy laws. Distributed learning offers a solution to this issue and will be demonstrated. Medical imaging combined with artificial intelligence will guide personalized cancer treatment in the future.
You may share our flyer with your colleagues.
COURSE IN THE CONTEXT OF
The Marie Curie Network PREDICT, the STW project STRATEGY, the Interreg project EURADIOMICS, Ducat-NWO
Our starting point is an overview of the history of Medical Imaging Artificial Intelligence we then discuss the success stories but also the pitfalls. Next, we will review the process from data acquisition, access to the DICOM objects, features extraction, machine learning (including new developments with Deep Learning) analysis and validation.
In the final part of the course, we will discuss the current challenges and directions of research in the field; in particular, the necessity of dealing with large annotated data sets, the FAIR principles and the distributed learning approach. The course will be divided into lectures during the morning and hands on assignments in the afternoon. Participants are encouraged to come with their data and we will organize (if possible) matching data for validation from other participants on the course.
Participants are encouraged to bring their datasets for analysis during the workshops. The dataset has to be fully open source (e.g. from TCIA) or anonymized and cleared by ethics (a written prove of this will be required). If requested in advance, the organisers will perform “data matching” for attendees to facilitate external cross validation. Please contact us to avail of this service.
Regarding Radiomics, Deep Learning and Distributed Learning, after this course you will be able to:
- explain the fundamentals
- critically evaluate the literature
- understand ‘the tools and tricks of the trade’
- provide advice in designing Quantitative Image Analysis Experiments
- make data FAIR (Findable, Accessible, Interoperable, Reusable)
- comply with regulation and privacy laws (DGPR)
- Philippe Lambin Maastricht University, The Netherlands (Course Director)
- Arthur Jochems, Maastricht University, The Netherlands (Organiser)
- Henry Woodruff, Maastricht University, The Netherlands (Organiser)
- Joe Deasy, MSKCC, USA
- Michel Dumontier, Maastricht University, The Netherlands
- Olivier Gevaert Stanford, USA
- Mathieu Hatt, LaTIM INSERM, France
- Ralph Leijenaar, Maastricht University, The Netherlands
- Olivier Morin, UCSF & Principal Investigator of the Morin QI Lab, California, USA (Organiser)
- Wiro Niessen, Erasmus MC, and Delft University of Technology, The Netherlands
- Mathias Prokop, Radboud UMC, The Netherlands – TBC
- David Townend, Maastricht University, The Netherlands
- Martin Vallières, McGill University, Canada
- Sean Walsh, Maastricht University, The Netherlands
- Joachim Wildberger, University Hospital Maastricht, The Netherlands
Decision Support Systems: of The D-Lab