Piazza access for registered students

Course description

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.

Course location

Online or CentraleSupelec


  1. Introduction to medical imaging
  2. 14/01 at 13h45
  3. Classification (with lab)
  4. 21/01 at 13h45
  5. Validation, interpretation and reproducibility (with lab)
  6. 28/01 at 13h45
  7. Detection (with lab)
  8. 04/02 at 13h45
  9. Segmentation (with lab)
  10. 23/02 at 13h45
  11. Generative models (autoencoders, GANs) (with lab)
  12. 09/03 at 13h45
  13. Denoising and reconstruction (with lab)
  14. 26/03 at 13h45
  15. Registration (with lab)
  16. 02/04 at 13h45


Olivier Colliot

Research Director, CNRS

Co-head, ARAMIS Lab
CNRS, Inria, Inserm, Sorbonne University and Paris Brain Institute

Chair, PRAIRIE Institute for Artificial Intelligence

Maria Vakalopoulou

Assistant Professor, CentraleSupelec

Researcher in deep learning and computer vision