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| Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network W Shi, J Caballero, F Huszár, J Totz, AP Aitken, R Bishop, D Rueckert, ... Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 6828 | 2016 |
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| 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 | 1256 | 2017 |
| Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy P Aljabar, RA Heckemann, A Hammers, JV Hajnal, D Rueckert Neuroimage 46 (3), 726-738, 2009 | 1136 | 2009 |
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| Secure, privacy-preserving and federated machine learning in medical imaging GA Kaissis, MR Makowski, D Rückert, RF Braren Nature Machine Intelligence 2 (6), 305-311, 2020 | 813 | 2020 |
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| Deep learning for cardiac image segmentation: a review C Chen, C Qin, H Qiu, G Tarroni, J Duan, W Bai, D Rueckert Frontiers in cardiovascular medicine 7, 25, 2020 | 698 | 2020 |
| 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 | 655 | 2018 |
| A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises SK Zhou, H Greenspan, C Davatzikos, JS Duncan, B Van Ginneken, ... Proceedings of the IEEE 109 (5), 820-838, 2021 | 654 | 2021 |
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Jo HajnalProfessor of Imaging Science, Kings College London在 kcl.ac.uk 的电子邮件经过验证
