Number of hours
- Lectures 12.0
- Projects 0
- Tutorials 0
- Internship 0
- Laboratory works 12.0
ECTS
ECTS 3.0
Goal(s)
Provide a solid introduction to machine-learning fundamentals, then delve into state-of-the-art deep-learning techniques and their practical applications in medical imaging
Contact Dawood AL CHANTIContent(s)
Introduction to basic and advanced deep-learning techniques for medical-image analysis
1. Machine-Learning Fundamentals: supervised classification & regression (linear and logistic)
2. Deep Feed-Forward Networks: fully connected (multilayer perceptron) architectures
3. Convolutional Neural Networks (CNNs): feature extraction for 2-D, 3-D and multi-modal images
4. Representation Learning with Autoencoders: latent-space modeling and anomaly detection
5. Explainable AI & Uncertainty Quantification: XAI methods tailored to clinical decision-making
6. Generative Models: GANs, VAEs and hybrid approaches for data augmentation and synthesis
7. Diffusion Models: state-of-the-art denoising and high-fidelity image generation
8. MedSAM: “Segment Anything” for Healthcare: prompt-based foundation models for universal medical-image segmentation
Prerequisites
Python language MANDATORY
1st year mathematics course
Semester 9 - The exam is given in english only
Continuous assessment
BE report + MCQ + Project
Rapport de BE : 50%
Examen Ecrit : 50%
Semester 9 - This course is given in english only
Deep Learning, An MIT Press book, Ian Goodfellow and Yoshua Bengio and Aaron Courville
https://www.deeplearningbook.org/