| 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 | 4312 | 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 | 1203 | 2019 |
| A deep cascade of convolutional neural networks for dynamic MR image reconstruction J Schlemper, J Caballero, JV Hajnal, AN Price, D Rueckert IEEE transactions on Medical Imaging 37 (2), 491-503, 2017 | 1139 | 2017 |
| Convolutional recurrent neural networks for dynamic MR image reconstruction C Qin, J Schlemper, J Caballero, AN Price, JV Hajnal, D Rueckert IEEE transactions on medical imaging 38 (1), 280-290, 2018 | 509 | 2018 |
| A deep cascade of convolutional neural networks for MR image reconstruction J Schlemper, J Caballero, JV Hajnal, A Price, D Rueckert Information Processing in Medical Imaging: 25th International Conference …, 2017 | 348 | 2017 |
| Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach J Duan, G Bello, J Schlemper, W Bai, TJW Dawes, C Biffi, A de Marvao, ... IEEE transactions on medical imaging 38 (9), 2151-2164, 2019 | 194 | 2019 |
| Joint learning of motion estimation and segmentation for cardiac MR image sequences C Qin, W Bai, J Schlemper, SE Petersen, SK Piechnik, S Neubauer, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 154 | 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 | 122 | 2018 |
| 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 | 108 | 2018 |
| Deep learning techniques for magnetic resonance image reconstruction J Schlemper, SSM Salehi, M Sofka, P Kundu, Z Wang, C Lazarus, ... US Patent US2020/0034998 A1, 2020 | 87 | 2020 |
| VS-Net: Variable splitting network for accelerated parallel MRI reconstruction J Duan, J Schlemper, C Qin, C Ouyang, W Bai, C Biffi, G Bello, B Statton, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 79 | 2019 |
| Deep learning techniques for suppressing artefacts in magnetic resonance images C Lazarus, P Kundu, S Tang, SSM Salehi, M Sofka, J Schlemper, ... US Patent US2020/0058106 A1, 2020 | 77 | 2020 |
| Unsupervised multi-modal style transfer for cardiac MR segmentation C Chen, C Ouyang, G Tarroni, J Schlemper, H Qiu, W Bai, D Rueckert Statistical Atlases and Computational Models of the Heart. Multi-Sequence …, 2020 | 76 | 2020 |
| Stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac MRI J Schlemper, G Yang, P Ferreira, A Scott, LA McGill, Z Khalique, ... International conference on medical image computing and computer-assisted …, 2018 | 61 | 2018 |
| Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination K Hammernik, J Schlemper, C Qin, J Duan, RM Summers, D Rueckert Magnetic Resonance in Medicine 86 (4), 1859-1872, 2021 | 55 | 2021 |
| Multi-coil magnetic resonance imaging using deep learning J Schlemper, SSM Salehi, M Sofka US Patent US 2020/0294287 A1, 2020 | 51 | 2020 |
| 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 18, 2018 | 48 | 2018 |
| Deep learning techniques for alignment of magnetic resonance images J Schlemper, SSM Salehi, M Sofka US Patent US 2020/0294282 A1, 2020 | 45 | 2020 |
| Self ensembling techniques for generating magnetic resonance images from spatial frequency data J Schlemper, SSM Salehi, M Sofka US Patent US2020/0294229 A1, 2020 | 43 | 2020 |
| Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging Q Meng, M Sinclair, V Zimmer, B Hou, M Rajchl, N Toussaint, O Oktay, ... IEEE transactions on medical imaging 38 (12), 2755-2767, 2019 | 43 | 2019 |
Daniel RueckertTechnical University of Munich and Imperial College LondonVerified email at tum.de
