| PILOT: Physics-informed learned optimized trajectories for accelerated MRI T Weiss, O Senouf, S Vedula, O Michailovich, M Zibulevsky, A Bronstein arXiv preprint arXiv:1909.05773, 2019 | 88 | 2019 |
| Towards CT-quality ultrasound imaging using deep learning S Vedula, O Senouf, AM Bronstein, OV Michailovich, M Zibulevsky arXiv preprint arXiv:1710.06304, 2017 | 48 | 2017 |
| Self-supervised learning of inverse problem solvers in medical imaging O Senouf, S Vedula, T Weiss, A Bronstein, O Michailovich, M Zibulevsky Domain Adaptation and Representation Transfer and Medical Image Learning …, 2019 | 37 | 2019 |
| High frame-rate cardiac ultrasound imaging with deep learning O Senouf, S Vedula, G Zurakhov, A Bronstein, M Zibulevsky, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 36 | 2018 |
| Joint learning of cartesian under sampling andre construction for accelerated mri T Weiss, S Vedula, O Senouf, O Michailovich, M Zibulevsky, A Bronstein ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 33* | 2020 |
| Learning beamforming in ultrasound imaging S Vedula, O Senouf, G Zurakhov, A Bronstein, O Michailovich, ... arXiv preprint arXiv:1812.08043, 2018 | 29 | 2018 |
| High quality ultrasonic multi-line transmission through deep learning S Vedula, O Senouf, G Zurakhov, A Bronstein, M Zibulevsky, ... Machine Learning for Medical Image Reconstruction: First International …, 2018 | 23 | 2018 |
| 3D FLAT: Feasible learned acquisition trajectories for accelerated MRI S Vedula, O Senouf, A Bronstein Machine Learning for Medical Image Reconstruction: 3rd Intern. WS MLMIR, 3, 2020 | 11 | 2020 |
| 3D FLAT: feasible learned acquisition trajectories for accelerated MRI J Alush-Aben, L Ackerman-Schraier, T Weiss, S Vedula, O Senouf, ... Machine Learning for Medical Image Reconstruction: Third International …, 2020 | 8 | 2020 |
| Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study T Mahendiran, D Thanou, O Senouf, D Meier, N Dayer, F Aminfar, ... Open Heart 10 (1), e002237, 2023 | 7 | 2023 |
| PILOT: Physics-informed learned optimized trajectories for accelerated MRI. arXiv 2019 T Weiss, O Senouf, S Vedula, O Michailovich, M Zibulevsky, A Bronstein arXiv preprint arXiv:1909.05773, 0 | 5 | |
| Towards Learned Optimal q-Space Sampling in Diffusion MRI T Weiss, S Vedula, O Senouf, O Michailovich, A Bronstein Computational Diffusion MRI: International MICCAI Workshop, Lima, Peru …, 2021 | 3 | 2021 |
| Anatomy-informed multimodal learning for myocardial infarction prediction ID Sievering, O Senouf, T Mahendiran, D Nanchen, S Fournier, O Muller, ... medRxiv, 2023.07. 11.23292509, 2023 | 1 | 2023 |
| Systems and methods for ultrasonic imaging O Senouf, S Vedula, A Bronstein, M Zibulevsky, G Zurakhov, ... US Patent 11,158,052, 2021 | 1 | 2021 |
| Predicting future myocardial infarction from angiographies with deep learning D Thanou, OY Senouf, O Raita, E Abbé, P Frossard, F Aminfar, ... Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 2021 | 1 | 2021 |
| Inferring Cardiovascular Biomarkers with Hybrid Model Learning O Senouf, J Behrmann, JH Jacobsen, P Frossard, E Abbe, A Wehenkel NeurIPS 2023 Workshop on Deep Learning and Inverse Problems, 2023 | | 2023 |
| Can Knowledge Transfer Techniques Compensate for the Limited Myocardial Infarction Data by Leveraging Hæmodynamics? An in silico Study R Tenderini, F Betti, OY Senouf, O Muller, S Deparis, A Buffa, E Abbé International Conference on Artificial Intelligence in Medicine, 218-228, 2023 | | 2023 |
| Attention-based learning of views fusion applied to myocardial infarction diagnosis from x-ray CT J Gwizdala, O Senouf, D Auberson, D Meier, D Rotzinger, S Fournier, ... MedNeurIPS, 2022 | | 2022 |
| GRAPH NEURAL NETWORK BASED FUTURE CLINICAL EVENTS PREDICTION FROM INVASIVE CORONARY ANGIOGRAPHY X Sun, T Belmpas, O Senouf, E Abbé, P Frossard, B De Bruyne, O Muller, ... | | |
| MDS1 RJ Berthier, A Cordonier, E Cornacchia, J Hazla, A Lotfi, PS Ralli, ... | | |
