| Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation … PP Sengupta, S Shrestha, B Berthon, E Messas, E Donal, GH Tison, ... Cardiovascular Imaging 13 (9), 2017-2035, 2020 | 161 | 2020 |
| Diagnosis of heart failure with preserved ejection fraction: machine learning of spatiotemporal variations in left ventricular deformation M Tabassian, I Sunderji, T Erdei, S Sanchez-Martinez, A Degiovanni, ... Journal of the American society of echocardiography 31 (12), 1272-1284. e9, 2018 | 109 | 2018 |
| Statistical shape modeling of the left ventricle: myocardial infarct classification challenge A Suinesiaputra, P Ablin, X Alba, M Alessandrini, J Allen, W Bai, S Cimen, ... IEEE journal of biomedical and health informatics 22 (2), 503-515, 2017 | 90 | 2017 |
| Knitted fabric defect classification for uncertain labels based on Dempster–Shafer theory of evidence M Tabassian, R Ghaderi, R Ebrahimpour Expert Systems with Applications 38 (5), 5259-5267, 2011 | 50 | 2011 |
| Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification M Tabassian, M Alessandrini, L Herbots, O Mirea, ED Pagourelias, ... The international journal of cardiovascular imaging 33, 1159-1167, 2017 | 36 | 2017 |
| Combining complementary information sources in the Dempster–Shafer framework for solving classification problems with imperfect labels M Tabassian, R Ghaderi, R Ebrahimpour Knowledge-Based Systems 27, 92-102, 2012 | 36 | 2012 |
| Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels M Tabassian, R Ghaderi, R Ebrahimpour Expert systems with applications 39 (2), 1698-1707, 2012 | 30 | 2012 |
| Using artificial intelligence to manage thrombosis research, diagnosis, and clinical management A Mishra, MZ Ashraf Seminars in thrombosis and hemostasis 46 (04), 410-418, 2020 | 25 | 2020 |
| Area of the pressure-strain loop during ejection as non-invasive index of left ventricular performance: a population study N Cauwenberghs, M Tabassian, L Thijs, WY Yang, FF Wei, P Claus, ... Cardiovascular ultrasound 17, 1-11, 2019 | 16 | 2019 |
| Proposed requirements for cardiovascular imaging-related machine learning evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation … PP Sengupta, S Shrestha, B Berthon, E Messas, E Donal, GH Tison, ... Epub 2020/09/12. https://doi. org/10.1016/j. jcmg. 2020.07. 015 PMID …, 0 | 14 | |
| Biventricular imaging markers to predict outcomes in non‐compaction cardiomyopathy: a machine learning study C Rocon, M Tabassian, M Dantas Tavares de Melo, ... ESC HEART FAILURE 7, 2431–2439, 2020 | 11 | 2020 |
| Handling missing strain (rate) curves using K-nearest neighbor imputation M Tabassian, M Alessandrini, R Jasaityte, L De Marchi, G Masetti, ... 2016 IEEE International Ultrasonics Symposium (IUS), 1-4, 2016 | 11 | 2016 |
| Clutter filtering using a 3D deep convolutional neural network M Tabassian, XR Hu, B Chakraborty, J D’hooge 2019 IEEE International Ultrasonics Symposium (IUS), 2114-2117, 2019 | 10 | 2019 |
| 3D convolutional neural network for segmentation of the urethra in volumetric ultrasound of the pelvic floor H Williams, L Cattani, W Li, M Tabassian, T Vercauteren, J Deprest, ... 2019 IEEE International Ultrasonics Symposium (IUS), 1473-1476, 2019 | 8 | 2019 |
| Principal component analysis for the classification of cardiac motion abnormalities based on echocardiographic strain and strain rate imaging M Tabassian, M Alessandrini, L De Marchi, G Masetti, N Cauwenberghs, ... Functional Imaging and Modeling of the Heart: 8th International Conference …, 2015 | 8 | 2015 |
| Automatic detection of myocardial infarction through a global shape feature based on local statistical modeling M Tabassian, M Alessandrini, P Claes, L De Marchi, D Vandermeulen, ... Statistical Atlases and Computational Models of the Heart. Imaging and …, 2016 | 5 | 2016 |
| Automatic detection of ischemic myocardium by spatio-temporal analysis of echocardiographic strain and strain rate curves M Tabassian, M Alessandrini, L Herbots, O Mirea, J Engvall, L De Marchi, ... 2015 IEEE International Ultrasonics Symposium (IUS), 1-4, 2015 | 4 | 2015 |
| Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation MS Hosseini, MH Moradi, M Tabassian, J D'hooge Computer Methods and Programs in Biomedicine 205, 106085, 2021 | 3 | 2021 |
| Machine learning for quality assurance of myocardial strain curves M Tabassian, O ZulaicaIglesias, S Ünlü, JU Voigt, J D'hooge 2018 IEEE International Ultrasonics Symposium (IUS), 1-4, 2018 | 3 | 2018 |
| Handling classification problems with imperfect labels using an evidence-based neural network ensemble M Tabassian, R Ghaderi, R Ebrahimpour International Journal of Innovative Computing, Information and Control 7 (12 …, 2011 | 3 | 2011 |
Reza EbrahimpourProfessor, Sharif University of Technology, IPM (SCS)在 sharif.edu 的电子邮件经过验证
