Radiomics, Deep Learning, Synthetic Data and Distributed Learning

a hands-on course

This course on Artificial Intelligence for Imaging is a unique opportunity to join a community of leading edge 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.

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.

 

More information and registration here

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 automatic software. These features are distilled through machine learning into ‘signatures’ that function as quantitative imaging biomarkers. Recently the radiomics approach has been enriched by Deep Learning methods, and both fields are profiting from the advancement of data augmentation and harmonization 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.