| Attributes and predictors of long COVID CH Sudre, B Murray, T Varsavsky, MS Graham, RS Penfold, RC Bowyer, ... Nature medicine 27 (4), 626-631, 2021 | 2631 | 2021 |
| Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations CH Sudre, W Li, T Vercauteren, S Ourselin, M Jorge Cardoso Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 2621 | 2017 |
| Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study LH Nguyen, DA Drew, MS Graham, AD Joshi, CG Guo, W Ma, RS Mehta, ... The Lancet Public Health 5 (9), e475-e483, 2020 | 2584 | 2020 |
| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1945 | 2018 |
| The future of digital health with federated learning N Rieke, J Hancox, W Li, F Milletari, HR Roth, S Albarqouni, S Bakas, ... NPJ digital medicine 3 (1), 1-7, 2020 | 1706 | 2020 |
| Real-time tracking of self-reported symptoms to predict potential COVID-19 C Menni, AM Valdes, MB Freidin, CH Sudre, LH Nguyen, DA Drew, ... Nature medicine 26 (7), 1037-1040, 2020 | 1527 | 2020 |
| The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 1136 | 2023 |
| A large annotated medical image dataset for the development and evaluation of segmentation algorithms AL Simpson, M Antonelli, S Bakas, M Bilello, K Farahani, B Van Ginneken, ... arXiv preprint arXiv:1902.09063, 2019 | 1006 | 2019 |
| The medical segmentation decathlon M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ... Nature communications 13 (1), 4128, 2022 | 847 | 2022 |
| Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS … MJ Cardoso, T Arbel, G Carneiro, T Syeda-Mahmood, JMRS Tavares, ... Springer, 2017 | 758 | 2017 |
| NiftyNet: a deep-learning platform for medical imaging E Gibson, W Li, C Sudre, L Fidon, DI Shakir, G Wang, Z Eaton-Rosen, ... Computer methods and programs in biomedicine 158, 113-122, 2018 | 687 | 2018 |
| Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis JD Rohrer, JM Nicholas, DM Cash, J Van Swieten, E Dopper, L Jiskoot, ... The Lancet Neurology 14 (3), 253-262, 2015 | 569 | 2015 |
| Privacy-preserving federated brain tumour segmentation W Li, F Milletarì, D Xu, N Rieke, J Hancox, W Zhu, M Baust, Y Cheng, ... Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019 | 568 | 2019 |
| Faciobrachial dystonic seizures: the influence of immunotherapy on seizure control and prevention of cognitive impairment in a broadening phenotype SR Irani, CJ Stagg, JM Schott, CR Rosenthal, SA Schneider, P Pettingill, ... Brain 136 (10), 3151-3162, 2013 | 473 | 2013 |
| Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia JD Rohrer, IOC Woollacott, KM Dick, E Brotherhood, E Gordon, A Fellows, ... Neurology 87 (13), 1329-1336, 2016 | 456 | 2016 |
| Deep gray matter volume loss drives disability worsening in multiple sclerosis A Eshaghi, F Prados, WJ Brownlee, DR Altmann, C Tur, MJ Cardoso, ... Annals of neurology 83 (2), 210-222, 2018 | 419 | 2018 |
| Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion MJ Cardoso, M Modat, R Wolz, A Melbourne, D Cash, D Rueckert, ... IEEE transactions on medical imaging 34 (9), 1976-1988, 2015 | 419 | 2015 |
| Rapid implementation of mobile technology for real-time epidemiology of COVID-19 DA Drew, LH Nguyen, CJ Steves, C Menni, M Freydin, T Varsavsky, ... Science 368 (6497), 1362-1367, 2020 | 418 | 2020 |
| Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies N Burgos, MJ Cardoso, K Thielemans, M Modat, S Pedemonte, J Dickson, ... IEEE transactions on medical imaging 33 (12), 2332-2341, 2014 | 418 | 2014 |
| On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task W Li, G Wang, L Fidon, S Ourselin, MJ Cardoso, T Vercauteren Information Processing in Medical Imaging: 25th International Conference …, 2017 | 407 | 2017 |
Sebastien OurselinProfessor of Healthcare Engineering, King's College LondonE-mail confirmado em kcl.ac.uk
