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M. Jorge Cardoso
M. Jorge Cardoso
Bestätigte E-Mail-Adresse bei kcl.ac.uk - Startseite
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Zitiert von
Jahr
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
24592020
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
24042021
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
23452017
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
17612018
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
14732020
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
14092020
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
9302023
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
9022019
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
6972017
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
6652018
The medical segmentation decathlon
M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ...
Nature communications 13 (1), 4128, 2022
6392022
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
5482015
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
4862019
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
4602013
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
4252016
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
4172020
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
4042015
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
3972014
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
3862017
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
3812018
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