Deep learning: basics and convolutional neural networks (CNN)

Maria Vakalopoulou (1) , Stergios Christodoulidis (1) , Ninon Burgos (2) , Olivier Colliot (2) , Vincent Lepetit (3)

1 CentraleSupélec
2 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
3 LIGM - Laboratoire d'Informatique Gaspard-Monge

Deep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components. However, most of them share similar modules and mathematical formulations. In this chapter, the basic concepts of deep learning will be presented to provide a better understanding of these powerful and broadly used algorithms. The analysis is structured around the main components of deep learning architectures, focusing on convolutional neural networks and autoencoders.

Mots clés: Perceptrons; Backpropagation; Convolutional neural networks; Deep learning; Medical imaging

Domaines: Machine Learning [stat.ML]; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Imagerie médicale; Traitement des images [eess.IV]; Imagerie; Traitement du signal et de l'image [eess.SP]

Maria Vakalopoulou, Stergios Christodoulidis, Ninon Burgos, Olivier Colliot, Vincent Lepetit. Deep learning: basics and convolutional neural networks (CNN). Olivier Colliot. Machine Learning for Brain Disorders, Springer, 2023. ⟨hal-03957224v1⟩