| Attention u-net: Learning where to look for the pancreas O Oktay, J Schlemper, LL Folgoc, M Lee, M Heinrich, K Misawa, K Mori, ... arXiv preprint arXiv:1804.03999, 2018 | 6328 | 2018 |
| Attention gated networks: Learning to leverage salient regions in medical images J Schlemper, O Oktay, M Schaap, M Heinrich, B Kainz, B Glocker, ... Medical image analysis 53, 197-207, 2019 | 1578 | 2019 |
| Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation O Oktay, E Ferrante, K Kamnitsas, M Heinrich, W Bai, J Caballero, ... IEEE transactions on medical imaging 37 (2), 384-395, 2017 | 813 | 2017 |
| Automated cardiovascular magnetic resonance image analysis with fully convolutional networks W Bai, M Sinclair, G Tarroni, O Oktay, M Rajchl, G Vaillant, AM Lee, ... Journal of cardiovascular magnetic resonance 20 (1), 65, 2018 | 697 | 2018 |
| Semi-supervised learning for network-based cardiac MR image segmentation W Bai, O Oktay, M Sinclair, H Suzuki, M Rajchl, G Tarroni, B Glocker, ... Medical Image Computing and Computer-Assisted Intervention− MICCAI 2017 …, 2017 | 486 | 2017 |
| Deepcut: Object segmentation from bounding box annotations using convolutional neural networks M Rajchl, MCH Lee, O Oktay, K Kamnitsas, J Passerat-Palmbach, W Bai, ... IEEE transactions on medical imaging 36 (2), 674-683, 2016 | 476 | 2016 |
| Attention u-net: Learning where to look for the pancreas. arXiv O Oktay, J Schlemper, LL Folgoc, M Lee, M Heinrich, K Misawa, K Mori, ... arXiv preprint arXiv:1804.03999 10, 2018 | 379 | 2018 |
| Multi-input cardiac image super-resolution using convolutional neural networks O Oktay, W Bai, M Lee, R Guerrero, K Kamnitsas, J Caballero, ... Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016: 19th …, 2016 | 284 | 2016 |
| White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks R Guerrero, C Qin, O Oktay, C Bowles, L Chen, R Joules, R Wolz, ... NeuroImage: Clinical 17, 918-934, 2018 | 229 | 2018 |
| Attention u-net: learning where to look for the pancreas (2018) O Oktay, J Schlemper, LL Folgoc, M Lee, M Heinrich, K Misawa, K Mori, ... arXiv preprint arXiv:1804.03999, 1804 | 220 | 1804 |
| Evaluating reinforcement learning agents for anatomical landmark detection A Alansary, O Oktay, Y Li, L Le Folgoc, B Hou, G Vaillant, K Kamnitsas, ... Medical image analysis 53, 156-164, 2019 | 193 | 2019 |
| Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing B Boecking, N Usuyama, S Bannur, DC Castro, A Schwaighofer, S Hyland, ... European Conference on Computer Vision (ECCV), 2022 | 191 | 2022 |
| Recurrent neural networks for aortic image sequence segmentation with sparse annotations W Bai, H Suzuki, C Qin, G Tarroni, O Oktay, PM Matthews, D Rueckert Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 147 | 2018 |
| Adversarial and perceptual refinement for compressed sensing MRI reconstruction M Seitzer, G Yang, J Schlemper, O Oktay, T Würfl, V Christlein, T Wong, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 133 | 2018 |
| Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study R Robinson, VV Valindria, W Bai, O Oktay, B Kainz, H Suzuki, ... Journal of Cardiovascular Magnetic Resonance 21, 1-14, 2019 | 126 | 2019 |
| Attention-gated networks for improving ultrasound scan plane detection J Schlemper, O Oktay, L Chen, J Matthew, C Knight, B Kainz, B Glocker, ... arXiv preprint arXiv:1804.05338, 2018 | 122 | 2018 |
| Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography O Bernard, JG Bosch, B Heyde, M Alessandrini, D Barbosa, ... IEEE transactions on medical imaging 35 (4), 967-977, 2015 | 114 | 2015 |
| Image-and-spatial transformer networks for structure-guided image registration MCH Lee, O Oktay, A Schuh, M Schaap, B Glocker Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 98 | 2019 |
| Stratified decision forests for accurate anatomical landmark localization in cardiac images O Oktay, W Bai, R Guerrero, M Rajchl, A De Marvao, DP O’Regan, ... IEEE transactions on medical imaging 36 (1), 332-342, 2016 | 87 | 2016 |
| Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling C Biffi, O Oktay, G Tarroni, W Bai, A De Marvao, G Doumou, M Rajchl, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 83 | 2018 |
Daniel RueckertTechnical University of Munich and Imperial College LondonVerified email at tum.de
