DESCRIPTION :
Objectives Propose and analyze learnable mathematical morphological operators using implicit differentiation techniques that are compatible with autodifferentiation combined with variational mathematical morphology.
Context The techniques outlined in the project topic are frequently used in medical image analysis, which constitutes a large part of the activity of the host laboratory (Inria OPIS team).
Methods The project topic builds on the expertise of the supervisory team in high-dimensional optimization methods, deep learning, and mathematical morphology. It is an extension of some of the work recently carried out in this team [4,2].
Expected Results New neural network architectures based on learnable variational mathematical morphological operators, software implementations in PyTorch/JAX.
Bibliography
[1] Shaojie Bai, J Zico Kolter, and Vladlen Koltun. Deep equilibrium models. Advances in neural information processing systems, 32, 2019.
[2] Jérôme Bolte, Tam Le, Edouard Pauwels, and Tony Silveti-Falls. Nonsmooth implicit differentiation for machine-learning and optimization. Advances in neural information processing systems, 34:13537-13549, 2021.
[3] Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, and Laurent El Ghaoui. Implicit graph neural networks. Advances in Neural Information Processing Systems, 33:11984-11995, 2020.
[4] Laurent Najman, Jean-Christophe Pesquet, and Hugues Talbot. When convex analysis meets mathematical morphology on graphs. In Mathematical Morphology and Its Applications to Signal and Image Processing: 12th International Symposium, ISMM 2015, Reykjavik, Iceland, May 27-29, 2015. Proceedings 12, pages 473-484. Springer, 2015.
[5] Santiago Velasco-Forero, Samy Blusseau, and Mateus Sangalli. Mathematical morphology meets deep learning. In 13th European Congress for Stereology and Image Analysis (ECSIA), 2024
Code d'emploi : Ingénieur Études et Développements IT (h/f)
Domaine professionnel actuel : IT R&D Professionals
Temps partiel / Temps plein : Plein temps
Type de contrat : Contrat à durée indéterminée (CDI)
Compétences : Réseaux de Neurones Artificiels, Vision par Ordinateur, Analyse d'Image, Python (Langage de Programmation), Machine Learning, Tensorflow, Traitement des Données, Pytorch, Deep Learning, Anglais, Traitement d'Image, Mathématiques, Recherche Post-Doctorale, Morphologie, Management d'Équipe
Courriel :
falls@inria.fr
Téléphone :
0139635511
Type d'annonceur : Employeur direct