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Deep Learning–Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography

, , , , , , , and . Journal of the American Heart Association, (April 2020)
DOI: 10.1161/jaha.119.014717

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Deep Learning–Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography, , , , , , , and . Journal of the American Heart Association, (April 2020)ECGT2T: Electrocardiogram synthesis from Two asynchronous leads to Ten leads., and . CoRR, (2021)On the Inductive Bias Transfer with Knowledge Distillation for Real-World Data., , and . AMLTS, volume 3375 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)Automatic Detection of Noisy Electrocardiogram Signals Without Explicit Noise Labels., , , and . ICPR Workshops (2), volume 13644 of Lecture Notes in Computer Science, page 634-643. Springer, (2022)Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram., , , , and . CHIL, volume 174 of Proceedings of Machine Learning Research, page 338-353. PMLR, (2022)Optimizing Neural Network Scale for ECG Classification., , and . CoRR, (2023)ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram., , , , and . CoRR, (2023)Efficient Data Augmentation Policy for Electrocardiograms., , , , and . CIKM, page 4153-4157. ACM, (2022)Text-to-ECG: 12-Lead Electrocardiogram Synthesis Conditioned on Clinical Text Reports., , , , , and . ICASSP, page 1-5. IEEE, (2023)ECGT2T: Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads., , , and . ICASSP, page 1-5. IEEE, (2023)