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Deep Learning for Model Correction in Cardiac Electrophysiological Imaging

hal.structure.identifierE-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
dc.contributor.authorKASHTANOVA, Victoriya
hal.structure.identifierThereSIS lab - Thales
hal.structure.identifierSorbonne Université [SU]
dc.contributor.authorAYED, Ibrahim
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
dc.contributor.authorARRIEULA, Andony
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
dc.contributor.authorPOTSE, Mark
hal.structure.identifierCriteo AI Lab
dc.contributor.authorGALLINARI, Patrick
hal.structure.identifierE-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
dc.contributor.authorSERMESANT, Maxime
dc.date.accessioned2024-04-04T02:41:14Z
dc.date.available2024-04-04T02:41:14Z
dc.date.conference2022-07-06
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191133
dc.description.abstractEnImaging the electrical activity of the heart can be achieved with invasive catheterisation. However, the resulting data are sparse and noisy. Mathematical modelling of cardiac electrophysiology can help the analysis but solving the associated mathematical systems can become unfeasible. It is often computationally demanding, for instance when solving for different patient conditions. We present a new framework to model the dynamics of cardiac electrophysiology at lower cost. It is based on the integration of a low-fidelity physical model and a learning component implemented here via neural networks. The latter acts as a complement to the physical part, and handles all quantities and dynamics that the simplified physical model neglects. We demonstrate that this framework allows us to reproduce the complex dynamics of the transmembrane potential and to correctly identify the relevant physical parameters, even when only partial measurements are available. This combined model-based and data-driven approach could improve cardiac electrophysiological imaging and provide predictive tools.
dc.description.sponsorship3IA Côte d'Azur - ANR-19-P3IA-0002
dc.description.sponsorshipL'Institut de Rythmologie et modélisation Cardiaque - ANR-10-IAHU-0004
dc.language.isoen
dc.subject.enElectrophysiology
dc.subject.enDeep learning
dc.subject.enSimulations
dc.subject.enPhysics-based learning
dc.title.enDeep Learning for Model Correction in Cardiac Electrophysiological Imaging
dc.typeCommunication dans un congrès
dc.subject.halInformatique [cs]
dc.subject.halMathématiques [math]
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halMathématiques [math]/Equations aux dérivées partielles [math.AP]
dc.description.sponsorshipEuropeElectrostructural Tomography - Towards Multiparametric Imaging of Cardiac Electrical Disorders
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleMIDL 2022 - Medical Imaging with Deep Learning
bordeaux.countryCH
bordeaux.conference.cityZurich
bordeaux.peerReviewedoui
hal.identifierhal-03687596
hal.version1
hal.invitednon
hal.proceedingsnon
hal.conference.end2022-07-08
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03687596v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=KASHTANOVA,%20Victoriya&AYED,%20Ibrahim&ARRIEULA,%20Andony&POTSE,%20Mark&GALLINARI,%20Patrick&rft.genre=unknown


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