Loic Le Folgoc
Loic Le Folgoc
Associate Professor, Télécom Paris, France
Verified email at
Cited by
Cited by
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
Attention u-net: Learning where to look for the pancreas. arXiv
O Oktay, J Schlemper, LL Folgoc, M Lee, M Heinrich, K Misawa, K Mori, ...
arXiv preprint arXiv:1804.03999 10, 2018
Evaluating reinforcement learning agents for anatomical landmark detection
A Alansary, O Oktay, Y Li, L Le Folgoc, B Hou, G Vaillant, K Kamnitsas, ...
Medical image analysis 53, 156-164, 2019
Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty
M Monteiro, L Le Folgoc, D Coelho de Castro, N Pawlowski, B Marques, ...
Advances in Neural Information Processing Systems 33, 12756-12767, 2020
Semi-supervised learning via compact latent space clustering
K Kamnitsas, D Castro, L Le Folgoc, I Walker, R Tanno, D Rueckert, ...
International Conference on Machine Learning, 2459-2468, 2018
Automatic view planning with multi-scale deep reinforcement learning agents
A Alansary, LL Folgoc, G Vaillant, O Oktay, Y Li, W Bai, ...
Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018
Explainable anatomical shape analysis through deep hierarchical generative models
C Biffi, JJ Cerrolaza, G Tarroni, W Bai, A De Marvao, O Oktay, C Ledig, ...
IEEE transactions on medical imaging 39 (6), 2088-2099, 2020
Quantifying registration uncertainty with sparse bayesian modelling
L Le Folgoc, H Delingette, A Criminisi, N Ayache
IEEE transactions on medical imaging 36 (2), 607-617, 2016
Is MC Dropout Bayesian?
LL Folgoc, V Baltatzis, S Desai, A Devaraj, S Ellis, OEM Manzanera, ...
arXiv preprint arXiv:2110.04286, 2021
Lifted auto-context forests for brain tumour segmentation
L Le Folgoc, AV Nori, S Ancha, A Criminisi
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016
Autosegmentation of prostate anatomy for radiation treatment planning using deep decision forests of radiomic features
MW Macomber, M Phillips, I Tarapov, R Jena, A Nori, D Carter, ...
Physics in Medicine & Biology 63 (23), 235002, 2018
Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements
L Le Folgoc, H Delingette, A Criminisi, N Ayache
Medical image analysis 36, 79-97, 2017
Attention u-net: Learning where to look for the pancreas. CoRR abs/1804.03999 (2018)
O Oktay, J Schlemper, LL Folgoc, MCH Lee, MP Heinrich, K Misawa, ...
arXiv preprint arXiv:1804.03999 760, 1804
Deep learning for cardiac motion estimation: supervised vs. unsupervised training
H Qiu, C Qin, L Le Folgoc, B Hou, J Schlemper, D Rueckert
Statistical Atlases and Computational Models of the Heart. Multi-Sequence …, 2020
Geometric deep learning for post-menstrual age prediction based on the neonatal white matter cortical surface
V Vosylius, A Wang, C Waters, A Zakharov, F Ward, L Le Folgoc, J Cupitt, ...
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and …, 2020
Segmentation of brain tumors via cascades of lifted decision forests
LL Folgoc, AV Nori, J Alvarez-Valle, R Lowe, A Criminisi
Proc. MICCAI-BRATS Workshop 2016, 35-39, 2016
Sparse bayesian registration
L Le Folgoc, H Delingette, A Criminisi, N Ayache
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014
Confidence-based training for clinical data uncertainty in image-based prediction of cardiac ablation targets
R Cabrera-Lozoya, J Margeta, L Le Folgoc, Y Komatsu, B Berte, J Relan, ...
International MICCAI Workshop on Medical Computer Vision, 148-159, 2014
A Variational Bayesian Method for Similarity Learning in Non-Rigid Image Registration
D Grzech, MF Azampour, B Glocker, J Schnabel, N Navab, B Kainz, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Training random decision trees for sensor data processing
AV Nori, A Criminisi, S Ancha, L Le Folgoc
US Patent App. 15/456,468, 2018
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