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EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology

Abstract : Cardiac electrophysiology models achieved good progress in simulating cardiac electrical activity. However, it is still challenging to leverage clinical measurements due to the discrepancy between idealised models and patient-specific conditions. In the last few years, data-driven machine learning methods have been actively used to learn dynamics and physical model parameters from data. In this paper, we propose a principled deep learning approach to learn the cardiac electrophysiology dynamics from data in the presence of scars in the cardiac tissue slab. We demonstrate that this technique is indeed able to reproduce the transmembrane potential dynamics in situations close to the training context. We then focus on evaluating the ability of the trained networks to generalize outside their training domain. We show experimentally that our model is able to generalize to new conditions including more complex scar geometries, multiple signal onsets and various conduction velocities.
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Contributor : Victoriya Kashtanova Connect in order to contact the contributor
Submitted on : Thursday, October 7, 2021 - 11:04:04 AM
Last modification on : Saturday, June 25, 2022 - 11:52:53 PM
Long-term archiving on: : Saturday, January 8, 2022 - 6:34:05 PM


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Victoriya Kashtanova, Ibrahim Ayed, Nicolas Cedilnik, Patrick Gallinari, Maxime Sermesant. EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology. FIMH 2021 - 11th International Conference on Functional Imaging and Modeling of the Heart, Jun 2021, Stanford, CA (virtual), United States. pp.482-492, ⟨10.1007/978-3-030-78710-3_46⟩. ⟨hal-03369201⟩



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