Olivier Colliot
Research Director, CNRS
Co-head, ARAMIS Lab CNRS, Inria, Inserm, Sorbonne University and Paris Brain Institute
Chair, PRAIRIE Institute for Artificial Intelligence
Piazza access for registered students (coming soon)
Medical imaging technologies provide unparalleled means to study structure and function of the human body in vivo. Interpretation of medical images is difficult due to the need to take into account three-dimensional, time-varying information from multiple types of medical images. Artificial intelligence (AI) holds great promises for assisting in the interpretation and medical imaging is one of the areas where AI is expected to lead to the most important successes. In the past years, deep learning technologies have led to impressive advances in medical image processing and interpretation.
This course covers both theoretical and practical aspects of deep learning for medical imaging. It covers the main tasks involved in medical image analysis (classification, segmentation, registration, generative models…) for which state-of-the-art deep learning techniques are presented, alongside some more traditional image processing and machine learning approaches. Examples of different types of medical imaging applications (brain, cardiac…) will also be provided.
CentraleSupelec
Research Director, CNRS
Co-head, ARAMIS Lab CNRS, Inria, Inserm, Sorbonne University and Paris Brain Institute
Chair, PRAIRIE Institute for Artificial Intelligence
Assistant Professor, CentraleSupelec
Researcher in deep learning and computer vision
PhD candidate
ARAMIS Lab CNRS, Inria, Inserm, Sorbonne University and Paris Brain Institute
PhD candidate
ARAMIS Lab and Corti/Corvol team CNRS, Inria, Inserm, Sorbonne University and Paris Brain Institute
PhD candidate
ARAMIS Lab and Epione project-team CNRS, Inria, Inserm, Sorbonne University and Paris Brain Institute