Publications of Victoriya Kashtanova

1. Victoriya Kashtanova. Learning cardiac electrophysiology dynamics with PDE-based physiological constraints for data-driven personalised predictions. Theses, Université Côte d'Azur, June 2023. Keyword(s): Physics-based learning, Deep learning, Cardiac electrophysiology, Model personalisation, PDE, Simulations, Apprentissage basé sur la physique, Apprentissage profond, Electrophysiologie cardiaque, Personnalisation de modèle, EDP, Simulations. [bibtex-entry]

1. Victoriya Kashtanova, Mihaela Pop, Ibrahim Ayed, Patrick Gallinari, and Maxime Sermesant. Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning. Interface Focus, 13(6), December 2023. Keyword(s): Physics-based learning, Deep Learning, Cardiac electrophysiology, Simulations. [bibtex-entry]

1. Victoriya Kashtanova, Ibrahim Ayed, Andony Arrieula, Mark Potse, Patrick Gallinari, and Maxime Sermesant. Deep Learning for Model Correction in Cardiac Electrophysiological Imaging. In MIDL 2022 - Medical Imaging with Deep Learning, Zurich, Switzerland, July 2022. Keyword(s): Electrophysiology, Deep learning, Simulations, Physics-based learning. [bibtex-entry]



2. Victoriya Kashtanova, Mihaela Pop, Ibrahim Ayed, Patrick Gallinari, and Maxime Sermesant. APHYN-EP: Physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics. In STACOM 2022 - 13th Workhop on Statistical Atlases and Computational Modelling of the Heart, Singapore, Singapore, September 2022. Keyword(s): Physics-based learning Deep Learning Electrophysiology Simulations, Physics-based learning, Deep Learning, Electrophysiology, Simulations. [bibtex-entry]



3. Victoriya Kashtanova, Ibrahim Ayed, Nicolas Cedilnik, Patrick Gallinari, and Maxime Sermesant. EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology. In FIMH 2021 - 11th International Conference on Functional Imaging and Modeling of the Heart, volume 12738 of Lecture Notes in Computer Science, Stanford, CA (virtual), United States, pages 482-492, June 2021. Springer International Publishing. Keyword(s): Electrophysiology, Deep learning, Simulation. [bibtex-entry]

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All person copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Les documents contenus dans ces répertoires sont rendus disponibles par les auteurs qui y ont contribué en vue d'assurer la diffusion à temps de travaux savants et techniques sur une base non-commerciale. Les droits de copie et autres droits sont gardés par les auteurs et par les détenteurs du copyright, en dépit du fait qu'ils présentent ici leurs travaux sous forme électronique. Les personnes copiant ces informations doivent adhérer aux termes et contraintes couverts par le copyright de chaque auteur. Ces travaux ne peuvent pas être rendus disponibles ailleurs sans la permission explicite du détenteur du copyright.

This document was translated from BibT_EX by bibtex2html