This summer school will be held at the École de Technologie Supérieure (ETS), Montreal, Canada.

The 2023 edition of the school will be held in Lyon, France. For more information, please click here.

A school for who?

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 anticipate a maximum of approximately 70 participants. Don't wait too long to register!

Please read carefully!

Since we anticipate that more than 70 people will register to our summer school, we decided to grant the possibility for some 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. This will come at a reduced cost of 100CAN$. Please keep in mind that virtual places are limited.

NEW INFORMATION

We have a few places left! Please do not wait to register. As soon as the maximum number of registrations will be reached, the registration page will be closed (and this can happen before the registration deadline)

Important dates

February 5th, 2022: Registration opening

May 16, 2022: [NEW REGISTRATION DEADLINE]

July 4-8, 2022: Summer school

July 6, 2022: Evening event

Registration fees

340CAN$ for students

440CAN$ for non-students

100CAN$ for virtual asynchronous participation

For the on-site participants, fees include 5 days of classes and hands-on sessions, coffee breaks, lunches, welcome cocktail and evening event at the museum. Please note that those fees do NOT include hotel.

NO REFUND POLICY: Please note that we shall provide NO REFUND under any circumstances.

What do I need to bring?

Those participating to the onsite event need to bring a laptop computer with a working WIFI connection. The hands on sessions will be held on the saturncloud.io online environment. So no need of a fancy hardware nor a specific operating system. And... oh yes! bring a happy face, it will be fun!

Previous editions

The previous editions of the school on deep learning for medical imaging were held in 2019 at the Université de Lyon, France and online in 2021. These schools were organized with the full logistic and financial support of the LabEx PRIMES and the CREATIS lab.

Organizing committee

Pierre-Marc Jodoin (Université de Sherbrooke, Canada)
Christian Desrosiers (École de technologie supérieure, Canada)
Jose Dolz (École de technologie supérieure, Canada)
Thomas Grenier (Université de Lyon, CREATIS, LabEx PRIMES, France)
Michaël Sdika (Université de Lyon, CREATIS, LabEx PRIMES, France)

Scientific committee (alphabetic order)

Olivier Bernard (Université de Lyon, CREATIS, LabEx PRIMES, France)
Christian Desrosiers (École de technologie supérieure, Canada)
Jose Dolz (École de technologie supérieure, Canada)
Nicolas Duchateau (Université de Lyon, CREATIS, LabEx PRIMES, France)
Rémi Émonet (Université de Lyon, CREATIS, LabEx PRIMES, France)
Thomas Grenier (Université de Lyon, CREATIS, LabEx PRIMES, France)
Pierre-Marc Jodoin (Université de Sherbrooke, Canada)
Carole Lartizien (Université de Lyon, CREATIS, LabEx PRIMES, France)
Fabien Millioz (Université de Lyon, CREATIS, LabEx PRIMES, France)
Bruno Montcel (Université de Lyon, CREATIS, LabEx PRIMES, France)
Emmanuel Roux (Université de Lyon, CREATIS, LabEx PRIMES, France)

Hands-on material