(please click here for the videos and slides of the oral presentations. Login and password was sent to you by e-mail. If not, please contact pierre-marc.jodoin [at] usherbrooke.ca or thomas.grenier [at] insa-lyon.fr)
Monday July 4
8h30 - 9h30 Registration and Breakfast
9h30 - 9h45 Welcome talk
9h45 - 10h30 Introduction to machine learning Part 1 (Pierre-Marc Jodoin)
Basics of machine learning, classification vs regression, train and test sets, metrics, over and under fitting, etc.
10h30 - 10h45 Coffee Break
10h45 - 12h00 Introduction to machine learning Part 2 (Pierre-Marc Jodoin)
Basics of machine learning, classification vs regression, train and test sets, metrics, over and under fitting, etc.
12h00 - 13h30 Lunch at the ETS
13h30 - 15h00 Basics in deep learning 1 (Pierre-Marc Jodoin)
Perceptron and multi-layer perceptron, stochastic gradient descent, learning rate, logistic regression, activation function, regularization (L1/L2/dropout/early stopping), etc.
15h00 - 15h30 Coffee break
15h30 - 17h00 Basics in deep learning 2 (Christian Desrosiers)
Weights initialization, forward and backward propagation, batch size, convolution neural nets (CNN), feature maps, pooling, pretraining and transfer learning, applications.
17h30 - 19h30 Cocktail at the ETS
Tuesday July 5
8h30 - 9h00 Breakfast
9h00 - 10h30 Advanced concepts in deep learning 1 (Michaël Sdika)
Common CNN architectures for classification (VGGNet, ResNet, ...) and localization (FasterRCNN, Yolo) and segmentation (encoder-Decoder, U-Net, ENet, ...)
10h30 - 11h00 Coffee Break
11h00 - 12h30 Generative and adversarial methods for medical imaging (Mohammad Havaei)
GANs, autoencoders and their training
12h30 - 14h00 Lunch at the ETS
15h00 - 15h30 Coffee break
14h00 - 17h00 Hands-on session 1: Introduction (Michaël Sdika, Thomas Grenier, Arash Ash, David Osowiechi)
Classification from machine learning to deep learning
Wednesday July 6
8h30 - 9h00 Breakfast
9h00 - 10h30 Advanced concepts in deep learning 2 (Hassan Rivaz)
Explainability, RNN, LSTM, Transformers, Self-supervised learning, AI-powered ultrasound, etc.
10h30 - 11h00 Coffee Break
11h00 - 12h30 Typical medical imaging issues (Samuel Kadoury)
Domain adaptation, privacy protection and federated learning, adversarial learning, common pitfalls, incomplete data, etc.
12h30 - 14h00 Lunch at the ETS
15h00 - 15h30 Coffee break
14h00 - 17h00 Hands-on session 2: Segmentation using deep learning (Michaël Sdika, Thomas Grenier, Arash Ash, Mélanie Gaillochet)
18h00 - 21h30 Museum visit and banquet dinner
Thursday July 7
8h30 - 9h00 Breakfast
9h00 - 10h30 Is my model interpretable, explainable, valid and useful? (Ryeyan Taseen)
10h30 - 11h00 Coffee Break
11h00 - 12h30 Round table (Ryeyan Taseen, Jean-René Bélanger, Laurent Létourneau-Guillon, Mohammad Havaei)
Why so much AI in research, why still so few AI in clinic?
12h30 - 14h00 Lunch at the ETS
15h00 - 15h30 Coffee break
14h00 - 17h00 Hands-on session 3: Variational Autoencoder (Pierre-Marc Jodoin, Gustavo Vargas, Mélanie Gaillochet, Shambhavi Mishra)
Auto-encoders, convolutional auto-encoders, variational auto-encoders, latent spaces
Friday July 8
8h30 - 9h00 Breakfast
9h00 - 10h30 Weakly supervised deep learning (Jose Dolz and Ismail Ben Ayed)
Weakly supervised segmentation, constrained CNN losses, semantic segmentation, semi-supervised learning
10h30 - 11h00 Coffee Break
11h00 - 12h30 Geometric deep learning (Hervé Lombaert)
Spectral coordinates and representation, spectral deep learning, brain surface matching and parcellation
12h30 - 12h45 Closing remarks
12h45 - 14h00 Lunch at the ETS
15h00 - 15h30 Coffee break
14h00 - 17h00 Hands-on session 4: Weakly supervised learning (Pierre-Marc Jodoin, Gustavo Vargas, Arash Ash, Christian Desrosiers and Jose Dolz)
