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Segmentation of the left ventricle in cardiac MRI using a hierarchical extreme learning machine model.

, , , , , , and . Int. J. Machine Learning & Cybernetics, 9 (10): 1741-1751 (2018)

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The virtual physiological human conference 2016, , and . Journal of computational science, (2018)Segmentation of the left ventricle in cardiac MRI using a hierarchical extreme learning machine model., , , , , , and . Int. J. Machine Learning & Cybernetics, 9 (10): 1741-1751 (2018)The Virtual Physiological Human Conference 2016, , and . Journal of computational science, (2018)Computational model for estimating the short- and long-term cardiac response to arteriovenous fistula creation for hemodialysis., , , , and . Med. Biol. Engineering and Computing, 50 (12): 1289-1298 (2012)Towards model-based analysis of cardiac MR tagging data: Relation between left ventricular shear strain and myofiber orientation., , , , and . Medical Image Analysis, 10 (4): 632-641 (2006)A Numerical Method of Reduced Complexity for Simulating Vascular Hemodynamics Using Coupled 0D Lumped and 1D Wave Propagation Models., , , and . Comp. Math. Methods in Medicine, (2012)Left Ventricular Shear Strain in Model and Experiment: The Role of Myofiber Orientation., , , , and . FIMH, volume 3504 of Lecture Notes in Computer Science, page 314-324. Springer, (2005)Quantitative Methods for Comparisons between Velocity Encoded MR-Measurements and Finite Element Modeling in Phantom Models., , , , , , , , and . International Conference on Computational Science (3), volume 2331 of Lecture Notes in Computer Science, page 255-264. Springer, (2002)Modeling regulation of vascular tone following muscle contraction: Model development, validation and global sensitivity analysis., , , , , and . J. Comput. Science, (2018)A fluid-structure interaction method with solid-rigid contact for heart valve dynamics., , and . J. Comput. Physics, 217 (2): 806-823 (2006)