MD, PhD, Professor of Radiology, Hôpital Européen Georges Pompidou, Université de Paris. Ecole Normale Supérieure (1991-1995), Research Fellow, UCSF, San Francisco, CA, USA (2001-2003). Responsible for the organisation of courses in Artificial Intelligence in Radiology for radiology residents, Member of the working group on Artificial Intelligence for the CERF – SFR, Member of the Scientific Committee of the DRIM France IA database. Grants over 650 k€ on radiomics and AI in medical imaging projects.
Topics of interest
Medical imaging, machine learning, radiomics, computer vision
Project in Prairie
Our project will focus on three approaches: 1) methodological developments on radiomics, i.e. high throughput extraction and selection of features from medical images using strategies including feature engineering, and deep learning and neural networks; 2) constitution of real-time prospective databases to obtain exploitable training and test data for the applications developed in Prairie; 3) integration of multimodality and multiparametric data stemming from multi-scale imaging going from microscopy to anatomical (radiology) and functional imaging.
Developments in computer vision need to translate into benefits for patients by transferring tools and applications developed for non-medical images to microscopic and macrsocopic medical imaging. The integration of this very diverse data to obtain a comprehensive view of a patient and his disease is a challenge which we must undertake in Prairie. The relative low numbers of patient data compared to the large number of features and parameters describing the patient and his disease, and the time-consuming annotation, remain important challenges and will require new tools which can train and learn on datasets with a weaker level of human supervision.