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.

Please note that this school has been labeled by the MICCAI Society and the French National Scientific Research Counsil (MaDICS research group).

We would also like to thank our sponsors for their generous contributions.

Venue and Accommodation


A big thank you to all of our sponsors!

Gold : LabexPrime, Diagnose, CHUS

Silver: Université de Sherbrooke, Google, INSAVALOR

Bronze: SIMS

  • LabEx primes
  • Diagnose Medical Systems
  • Centre Hospitalier Universitaire de Sherbrooke
  • Université de Sherbrooke
  • Google
  • CIMS
  • ETS
  • MICCAI Society
  • Madics


  • 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/

  • 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 École de Technologie Supérieure (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 the University of Lyon Electrical Engineering department and at the CREATIS lab in Lyon, France.

    His 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. He is now a professor at the École de Technologie Supérieure (ETS). His research interests include machine learning, deep learning, and reinforcement learning with application to data analytics.

  • Nicolas Thome

    Nicolas Thome

    Sorbonne University (Paris)

    His research interests include machine learning and deep learning for understanding low-level signals, e.g. vision, time series, acoustics, etc. He also explores solutions for combining low-level and higher-level data for multi-modal data processing. His current application domains are essentially targeted towards healthcare, physics and autonomous vehicles.

    He is involved in several French, European and international collaborative research projects on artificial intelligence and deep learning.

  • Mohammad Havaei

    Mohammad Havaei


    Mohammad Havaei is a Senior Research Scientist at Google. His research interest is focused on algorithmic fairness especially in the context of transfer learning. Mohammad's background is in the areas of computer vision, generative models and transfer learning. Before joining Google, he was a Research Scientist at Imagia where he focused on developing machine learning models applicable to medical image analysis.

  • Eric Kerfoot

    Eric Kerfoot

    King's College London

    Dr Eric Kerfoot is a Software Architect in Medical Engineering in the Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences.


Ecole de technologie supérieure de Montréal

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

Registration period

January 15, 2024 - 21:00 until May 28, 2024 - 11:00

Contact us

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

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