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Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.

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第一作者： Özgün,Turgut

第一单位： School of Computation, Information and Technology, Technical University of Munich, Germany; School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany. Electronic address: oezguen.turgut@tum.de.

作者： Özgün,Turgut ^[1] ; Philip,Müller ^[2] ; Paul,Hager ^[2] ; Suprosanna,Shit ^[3] ; Sophie,Starck ^[2] ; Martin J,Menten ^[4] ; Eimo,Martens ^[5] ; Daniel,Rueckert ^[6]

作者单位： School of Computation, Information and Technology, Technical University of Munich, Germany; School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany. Electronic address: oezguen.turgut@tum.de. ^[1] School of Computation, Information and Technology, Technical University of Munich, Germany; School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany. ^[2] Department of Quantitative Biomedicine, University of Zurich, Switzerland. ^[3] School of Computation, Information and Technology, Technical University of Munich, Germany; Munich Center for Machine Learning, Munich, Germany; Department of Computing, Imperial College London, United Kingdom. ^[4] School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany. ^[5] School of Computation, Information and Technology, Technical University of Munich, Germany; School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany; Munich Center for Machine Learning, Munich, Germany; Department of Computing, Imperial College London, United Kingdom. ^[6]

关键词 Cardiac magnetic resonance imaging Electrocardiogram Masked data modelling Multimodal contrastive learning Self-supervised learning

医学主题词人类(Humans);心电描记术(Electrocardiography);磁共振成像(Magnetic Resonance Imaging);心血管疾病(Cardiovascular Diseases);图像解释, 计算机辅助(Image Interpretation, Computer-Assisted)

DOI 10.1016/j.media.2024.103451

PMID 39793216

发布时间 2025-04-30

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