| Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge A Lalande, Z Chen, T Pommier, T Decourselle, A Qayyum, M Salomon, ... Medical Image Analysis 79, 102428, 2022 | 38 | 2022 |
| A 3D network based shape prior for automatic myocardial disease segmentation in delayed-enhancement MRI K Brahim, A Qayyum, A Lalande, A Boucher, A Sakly, F Meriaudeau IRBM 42 (6), 424-434, 2021 | 10 | 2021 |
| An improved 3d deep learning-based segmentation of left ventricular myocardial diseases from delayed-enhancement mri with inclusion and classification prior information u-net … K Brahim, TW Arega, A Boucher, S Bricq, A Sakly, F Meriaudeau Sensors 22 (6), 2084, 2022 | 9 | 2022 |
| A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases K Brahim, A Qayyum, A Lalande, A Boucher, A Sakly, F Meriaudeau 2020 Tenth International Conference on Image Processing Theory, Tools and …, 2020 | 5 | 2020 |
| A deep learning approach for the segmentation of myocardial diseases K Brahim, A Qayyum, A Lalande, A Boucher, A Sakly, F Meriaudeau 2020 25th International Conference on Pattern Recognition (ICPR), 4544-4551, 2021 | 4 | 2021 |
| Efficient 3D deep learning for myocardial diseases segmentation K Brahim, A Qayyum, A Lalande, A Boucher, A Sakly, F Meriaudeau Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC …, 2021 | 4 | 2021 |
| Spatio-temporal saliency detection using objectness measure K Brahim, R Kalboussi, M Abdellaoui, A Douik Signal, Image and Video Processing 13, 1055-1062, 2019 | 4 | 2019 |
| Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge. Medical Image Analysis, 2022 | | 2022 |
| Deep learning architectures for automatic detection of viable myocardiac segments K Brahim Bourgogne Franche-Comté, 2021 | | 2021 |
