July 8-12, 2024

2024 Summer school on deep learning for medical imaging, 5th Edition

Welcome to the 2024 summer school on deep learning for medical imaging!

This school is intended for medical imaging beginners and experts (students, post-docs, research professionals, and professors) who are eager to discover the fundamentals of deep learning and how it translates to medical imaging. We will walk you through the basics of machine learning all the way to the latest deep learning breakthroughs applied to medical imaging. As shown in the planning, the school has both oral presentations (15 hours) and 4 hands-on sessions of 3 hours each. During the hands-on sessions, the participants will be guided through the dos and don'ts of machine learning programs for medical imaging. Please see the program for more details.

Should you be a machine learning / deep learning expert to attend the school? NO! That is the point of this school!

Should you be a programming expert to attend the hands-on session? NO! Only basic skills in Python programming are required.

We have enough room to accommodate 70 on-site participants. So do not wait and register!

Since we anticipate that more than 70 people will register to our summer school, we will grant the possibility for people to virtually attend the school in an asynchronous manner. These virtual participants will not be permitted on-site participation but will have access to the recorded videos as well as the hands-on session material towards the end of the school.

Venue and Accommodation


  • Ismail Ben Ayed

    Ismail Ben Ayed

    École de Technologie Supérieure, Canada

    Dr. Ben Ayed received the PhD degree (with the highest honor) in computer vision from the INRS-EMT, Montreal in 2007. He is currently an Associate Professor at the ETS, University of Quebec. Before joining the ETS, he worked for 8 years as a research scientist at GE Healthcare, London, ON. He also holds an adjunct professor appointment at Western University (since 2012). Ismail's research interests include computer vision, optimization, machine learning and their potential applications in medical image analysis. He co-authored a book, over eighty peer-reviewed publications, mostly published in the top venues in these subject areas, and six US patents.

  • Pierre-Marc Jodoin

    Pierre-Marc Jodoin

    Université de Sherbrooke

    Pierre-Marc Jodoin is from the University of Sherbrooke, Canada where he works as a full professor since 2007. He specializes in the development of novel techniques for machine learning and deep learning applied to computer vision and medical imaging. He mostly works in video analytics and brain and cardiac image analytics. He is the co-director of the Sherbrooke AI platform and co-founder of the medical imaging company called "Imeka.ca" which specializes in MRI brain image analytics. web site: http://info.usherbrooke.ca/pmjodoin/

  • Hervé Lombaert

    Hervé Lombaert

    École Polytechnique de Montréal

    Herve Lombaert is Associate Professor of Computer Engineering at ETS Montreal and holds a Canada Research Chair in Shape Analysis in Medical Imaging. His research interests focus in Statistics on Shapes, Data & Medical Images, using graph analysis with applications in brain and cardiac imaging. He had the chance to work in multiple centers, including Microsoft Research (Cambridge, UK), Siemens Corporate Research (Princeton, NJ), Inria Sophia-Antipolis (France), McGill University (Canada), and Polytechnique Montreal (Canada). He is also a recipient of the François Erbsmann Prize, a top prize in Medical Image Analysis.

  • Christian Desrosiers

    Christian Desrosiers

    École de technologie supérieure

    Prof. Desrosiers obtained a Ph.D. in Applied Mathematics from Polytechnique Montreal in 2008, and was a postdoctoral researcher at the University of Minnesota with prof George Karypis. In 2009, he joined École de technologie supérieure (ÉTS) as professor in the Departement of Software and IT Engineering. He is codirector of the Laboratoire d’imagerie, de vision et d’intelligence artificielle (LIVIA) and a member of the REPARTI research network. He has over 100 publications in the fields of machine learning, image processing, computer vision and medical imaging, and has served on the scientific committee of several important conferences in these fields.

  • Jose Dolz

    Jose Dolz

    École de technologie supérieure, Canada

    Jose Dolz is Associate Professor in the Department of Software and IT Engineering at the ETS Montreal. Prior to be appointed Assistant Professor, he was a post-doctoral fellow at the ETS Montreal. Dr. Dolz obtained his B.Sc and M.Sc in the Polytechnic University of Valencia, Spain, and his Ph.D. at the University of Lille 2, France, in 2016. Dr. Dolz was recipient of a Marie-Curie FP7 Fellowship (2013-2016) to pursue his doctoral studies. His current research focuses on deep learning, medical imaging, optimization and learning strategies with limited supervision. He authored over 50 fully peer-reviewed papers, many of which published in the top venues in medical imaging (MedIA/TMI/MICCAI/IPMI), vision (CVPR) and learning (NeurIPS, ICML).

  • Michaël Sdika

    Michaël Sdika

    University of Lyon, France

    Michaël Sdika is a full member of the CREATIS lab in Lyon, France. His current research field focuses on the development of new analysis method based on deep learning for medical data. His main contributions are centered around image registration, atlas based segmentation, structure localization and machine learning for MR image of the nervous central system.

  • Thomas Grenier

    Thomas Grenier

    University of Lyon, France

    Dr. Thomas Grenier is Associate Professor at INSA Lyon Electrical Engineering department and at the CREATIS lab in Lyon, France.

    My research focuses on longitudinal analysis of medical data to study evolution as Multiple Sclerosis lesions, functional activity (muscle and hydrocephaly). Most of these studies involve a segmentation task and dedicated pre and post processing steps. Clustering (spatio-temporal mean-shift), semi-supervised (multi-atlas with machine learning) or fully supervised (DNN) schemes are used to solve such problems considering their specific constraints.

  •  Mohammadhadi Shateri

    Mohammadhadi Shateri


    Mohammadhadi Shateri (Member, IEEE) received the B.Sc. degree (with Hons.) in electrical engineering from the Amirkabir University of Technology, Tehran, Iran, in 2012, the M.Sc. degree (with Hons.) in electrical engineering from the University of Manitoba, Winnipeg, MB, Canada, in 2017, and the Ph.D. in electrical engineering from McGill University, Montreal, QC, Canada, in 2021.,His research interests include machine learning, deep learning, and reinforcement learning with application to data analytics.


  • Université de Sherbrooke
  • ETS
  • LabEx primes


Ecole de technologie supérieure de Montréal

1111, Rue Notre-Dame Ouest Montréal, Québec Canada, H3C 1K3

Contact us

If you have any questions, please contact pierre-marc.jodoin@usherbrooke.ca .

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