Deep Learning Fundamentals and Health Applications

 08/04/2026 (14-16)

Deep Learning Fundamentals I

deep neural networks; gradient issues; activation functions; normalization and regularization; optimization

Deep Learning Fundamentals II

neural autoencoders; unsupervised deep learning; autoencoding tasks in healthcare (anomaly detection, compression, denoising)

[6] Survey on autoencoders

[7,8] Surveys on autoencoders use in healthcare

Convolutional Neural Networks I

Introduction to medical imaging; basic CNN elements; 

Convolutional Neural Networks II

CNN architectures; medical imaging tasks

[SD] Chapter 10

[AI4H] Pg. 643-655

Additional readings

[9] Augmentation techniques for medical imaging

[10] Survival analysis with CNNs

[11] Large-scale medical image segmentation dataset and application

Software

nnU-net - Well engineered framework for low-code medical segmentation tasks

Deep learning for sequential data I

definitions; sequential data in healthcare; physiological timeseries; electronic health records; recurrent neural networks; learning long-term relationships and gradient issues

Coverage of  course books on this lecture is inadequate. You can use the course slides for this topic, and if you like you can integrate those with chapter 10 from the Deep Learning Book.

You can skip Sections 10.2.2, 10.2.3, 10.2.6)

Physionet- the reference repository for biomedical signals/timeseries

MIMIC-IV - the reference EHR dataset (in its latest version), also part of Physionet

Deep learning for sequential data II

gated recurrent networks; LSTMs and GRUs; bidirectional models; convolutional RNNs; applications of RNNs in healthcare

Coverage of  course books on this lecture is inadequate. You can use the course slides for this topic, and if you like you can integrate those with chapter 10 from the Deep Learning Book.

You can skip Sections 10.2.2, 10.2.3, 10.2.6)

Encoder-Decoder  Architectures and Neural Attention

sequence-to-sequence learning,  encoder-decoder architectures, cross-attention; self-attention; transformer architectures

(The lecture will conclude strictly at 15.30 to respect the Student Elections suspension of didactic activities. Please be in the classroom at 14.05 sharp!)

Deep learning with textual sequences I

representing textual information, word embeddings, skip-grams, tackling textual modelling tasks, masked language modelling

[SD] Chapter 12

[AI4H] Pg. 623-636

Deep learning with textual sequences II

relevant language model architectures, pretraining and fine tuning, language models in healthcare, foundation models