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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
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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
24382020
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
23712021
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
23002017
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
17412018
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
14692020
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
13782020
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
9112023
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
8882019
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
6942017
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
6612018
The medical segmentation decathlon
M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ...
Nature communications 13 (1), 4128, 2022
6152022
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
5462015
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
4772019
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
4592013
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
4242016
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
4152020
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
4012015
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
3922014
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
3812017
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
3772018
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