École d'été Deep Learning for Medical Imaging, 5th ed.

22 Février 2024

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Catégorie : Ecole thématique

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https://event.fourwaves.com/dlmi2024/pages

Monday July 8th

8h00 - 9h00 Registration and Breakfast
9h00 - 9h15 Welcome talk
9h15 - 10h30 Basics of deep learning Part 1
Perceptron and multi-layer perceptron, stochastic gradient descent, learning rate, logistic regression, activation function, regularization (L1/L2/dropout/early stopping), etc.
10h30 - 10h45 Coffee Break
10h45 - 12h00 Basics of deep learning Part 2
Perceptron and multi-layer perceptron, stochastic gradient descent, learning rate, logistic regression, activation function, regularization (L1/L2/dropout/early stopping), etc.

12h00 - 13h30 Lunch at the ETS

13h30 - 15h00 Basics of deep learning Part 3
Weights initialization, forward and backward propagation, batch size, convolution neural nets (CNN), feature maps, pooling, pretraining and transfer learning, applications
15h00 - 15h30 Coffee break
15h30 - 17h00 Convolutional neural networks
Common CNN architectures for classification (VGGNet, ResNet, ...) and localization (FasterRCNN, Yolo) and segmentation (encoder-Decoder, U-Net, ENet, ...)

17h30 - 19h30 Cocktail at the ETS

Tuesday, July 9

8h30 - 9h00 Breakfast
9h00 - 10h30 Generative and adversarial methods for medical imaging
GANs, autoencoders, VAE and their application to medical imaging.
10h30 - 11h00 Coffee Break
11h00 - 12h30 Advanced concepts in deep learning Part 1
RNN, Attention, Transformers, ViT, etc.

12h30 - 14h00 Lunch at the ETS

15h00 - 15h30 Coffee break
14h00 - 16h30 Hands-on session 1: Introduction
Classification from machine learning to deep learning
16h30 - 18h00 Poster session

Wednesday, July 10

8h30 - 9h00 Breakfast
9h00 - 10h30 Uncertainty and explainability

Quality, uncertainty, calibration, explainability.

10h30 - 11h00 Coffee Break
11h00 - 12h30 Typical medical imaging issues
Domain adaptation, privacy protection and federated learning, adversarial learning, common pitfalls, incomplete data, etc.

12h30 - 14h00 Lunch at the ETS
14h00 - 17h00 Hands-on session 2: Segmentation using deep learning

Thursday, July 11

8h30 - 9h00 Breakfast
9h00 - 10h30 Weakly supervised deep learning
Weakly supervised segmentation, constrained CNN losses, semantic segmentation, semi-supervised learning
10h30 - 11h00 Coffee Break
11h00 - 12h30 Round table
What are the challenges for AI to break into clinic?

12h30 - 14h00 Lunch at the ETS
14h00 - 17h00 Hands-on session 3: Variational Autoencoders
Auto-encoders, convolutional auto-encoders, variational auto-encoders, latent spaces

18h00 - 21h30 Museum visit and banquet dinner

Friday, July 12

8h30 - 9h00 Breakfast
9h00 - 10h30 Advanced concepts in deep learning Part 2
Foundation models, text-image models, CLIP, diffusion networks, Dall-E, etc.
10h30 - 11h00 Coffee Break
11h00 - 12h30 Geometric deep learning
Spectral coordinates and representation, spectral deep learning, brain surface matching and parcellation
12h30 - 12h45 Closing remarks

12h45 - 14h00 Lunch at the ETS
14h00 - 17h00 Hands-on session 4: Weakly supervised learning