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Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks.

, , , , , , , , , , , and . Medical Imaging: Image Processing, volume 10133 of SPIE Proceedings, page 101330S. SPIE, (2017)

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Automatic cine-based detection of patients at high risk of heart failure with reduced ejection fraction in echocardiograms., , , , , , , , , and . Comput. methods Biomech. Biomed. Eng. Imaging Vis., 8 (5): 502-508 (2020)Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks., , , , , , , , , and 2 other author(s). Medical Imaging: Image Processing, volume 10133 of SPIE Proceedings, page 101330S. SPIE, (2017)Automatic Quality Assessment of Echocardiograms Using Convolutional Neural Networks: Feasibility on the Apical Four-Chamber View., , , , , , , , , and 2 other author(s). IEEE Trans. Medical Imaging, 36 (6): 1221-1230 (2017)Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography., , , , , , , , , and . DLMIA/ML-CDS@MICCAI, volume 11045 of Lecture Notes in Computer Science, page 65-73. Springer, (2018)Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms., , , , , , , , , and . DLMIA/ML-CDS@MICCAI, volume 10553 of Lecture Notes in Computer Science, page 100-108. Springer, (2017)Quality Assessment of Echocardiographic Cine Using Recurrent Neural Networks: Feasibility on Five Standard View Planes., , , , , , , , and . MICCAI (3), volume 10435 of Lecture Notes in Computer Science, page 302-310. Springer, (2017)