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2020 – today
- 2024
[c18]Daniel Grzech, Loïc Le Folgoc, Mohammad Farid Azampour, Athanasios Vlontzos, Ben Glocker, Nassir Navab, Julia A. Schnabel, Bernhard Kainz:
Unsupervised Similarity Learning for Image Registration with Energy-Based Models. WBIR 2024: 229-240- [i14]Maxime Seince, Loïc Le Folgoc, Luiz Augusto Facury de Souza, Elsa D. Angelini:
Dense Self-Supervised Learning for Medical Image Segmentation. CoRR abs/2407.20395 (2024) - 2022
- [c17]Daniel Grzech, Mohammad Farid Azampour, Ben Glocker, Julia A. Schnabel, Nassir Navab, Bernhard Kainz
, Loïc Le Folgoc:
A variational Bayesian method for similarity learning in non-rigid image registration. CVPR 2022: 119-128 - 2021
- [c16]Octavio E. Martinez Manzanera, Sam Ellis, Vasileios Baltatzis, Arjun Nair, Loïc Le Folgoc, Sujal Desai, Ben Glocker, Julia A. Schnabel:
Patient-Specific 3d Cellular Automata Nodule Growth Synthesis In Lung Cancer Without The Need Of External Data. ISBI 2021: 5-9 - [c15]Vasileios Baltatzis, Loïc Le Folgoc, Sam Ellis, Octavio E. Martinez Manzanera, Kyriaki-Margarita Bintsi, Arjun Nair, Sujal Desai, Ben Glocker, Julia A. Schnabel:
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data. iMIMIC/TDA4MedicalData@MICCAI 2021: 56-64 - [c14]Vasileios Baltatzis, Kyriaki-Margarita Bintsi, Loïc Le Folgoc, Octavio E. Martinez Manzanera, Sam Ellis, Arjun Nair, Sujal Desai, Ben Glocker, Julia A. Schnabel:
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification. PRIME@MICCAI 2021: 201-211 - [i13]Loïc Le Folgoc, Vasileios Baltatzis, Amir Alansary, Sujal Desai, Anand Devaraj, Sam Ellis, Octavio E. Martinez Manzanera, Fahdi Kanavati, Arjun Nair, Julia A. Schnabel, Ben Glocker:
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data. CoRR abs/2108.00250 (2021) - [i12]Vasileios Baltatzis, Loïc Le Folgoc, Sam Ellis, Octavio E. Martinez Manzanera, Kyriaki-Margarita Bintsi, Arjun Nair, Sujal Desai, Ben Glocker, Julia A. Schnabel:
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data. CoRR abs/2108.04815 (2021) - [i11]Vasileios Baltatzis, Kyriaki-Margarita Bintsi, Loïc Le Folgoc, Octavio E. Martinez Manzanera, Sam Ellis, Arjun Nair, Sujal Desai, Ben Glocker, Julia A. Schnabel:
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification. CoRR abs/2108.05386 (2021) - [i10]Loïc Le Folgoc, Vasileios Baltatzis, Sujal Desai, Anand Devaraj, Sam Ellis, Octavio E. Martinez Manzanera, Arjun Nair, Huaqi Qiu, Julia A. Schnabel, Ben Glocker:
Is MC Dropout Bayesian? CoRR abs/2110.04286 (2021) - [i9]Daniel Grzech, Mohammad Farid Azampour, Huaqi Qiu, Ben Glocker, Bernhard Kainz, Loïc Le Folgoc:
Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo. CoRR abs/2110.13289 (2021) - 2020
- [j4]Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni, Wenjia Bai, Antonio de Marvao, Ozan Oktay, Christian Ledig, Loïc Le Folgoc, Konstantinos Kamnitsas, Georgia Doumou, Jinming Duan, Sanjay K. Prasad, Stuart A. Cook, Declan P. O'Regan, Daniel Rueckert:
Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models. IEEE Trans. Medical Imaging 39(6): 2088-2099 (2020) - [c13]Daniel Grzech, Bernhard Kainz, Ben Glocker, Loïc Le Folgoc:
Image Registration via Stochastic Gradient Markov Chain Monte Carlo. UNSURE/GRAIL@MICCAI 2020: 3-12 - [c12]Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loïc Le Folgoc, John Cupitt, Antonios Makropoulos, Andreas Schuh, Daniel Rueckert, Amir Alansary:
Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface. UNSURE/GRAIL@MICCAI 2020: 174-186 - [c11]Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker:
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. NeurIPS 2020 - [i8]Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker:
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. CoRR abs/2006.06015 (2020) - [i7]Loïc Le Folgoc, Vasileios Baltatzis, Amir Alansary, Sujal Desai, Anand Devaraj, Sam Ellis, Octavio E. Martinez Manzanera, Fahdi Kanavati, Arjun Nair, Julia A. Schnabel, Ben Glocker:
Bayesian Sampling Bias Correction: Training with the Right Loss Function. CoRR abs/2006.13798 (2020) - [i6]Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loïc Le Folgoc, John Cupitt, Antonios Makropoulos, Andreas Schuh, Daniel Rueckert, Amir Alansary:
Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface. CoRR abs/2008.06098 (2020)
2010 – 2019
- 2019
- [j3]Amir Alansary, Ozan Oktay, Yuanwei Li, Loïc Le Folgoc, Benjamin Hou, Ghislain Vaillant, Konstantinos Kamnitsas, Athanasios Vlontzos, Ben Glocker, Bernhard Kainz, Daniel Rueckert:
Evaluating reinforcement learning agents for anatomical landmark detection. Medical Image Anal. 53: 156-164 (2019) - [c10]Loïc Le Folgoc, Daniel Coelho de Castro, Jeremy Tan, Bishesh Khanal, Konstantinos Kamnitsas, Ian Walker, Amir Alansary, Ben Glocker:
Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing. IPMI 2019: 221-234 - [c9]Huaqi Qiu, Chen Qin, Loïc Le Folgoc, Benjamin Hou, Jo Schlemper, Daniel Rueckert:
Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training. STACOM@MICCAI 2019: 186-194 - [i5]Loïc Le Folgoc, Daniel Coelho de Castro, Jeremy Tan, Bishesh Khanal, Konstantinos Kamnitsas, Ian Walker, Amir Alansary, Ben Glocker:
Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing. CoRR abs/1903.02429 (2019) - [i4]Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni, Wenjia Bai, Ozan Oktay, Loïc Le Folgoc, Konstantinos Kamnitsas, Antonio de Marvao, Georgia Doumou, Jinming Duan, Sanjay K. Prasad, Stuart A. Cook, Declan P. O'Regan, Daniel Rueckert:
Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodeling. CoRR abs/1907.00058 (2019) - 2018
- [c8]Konstantinos Kamnitsas, Daniel Coelho de Castro, Loïc Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya V. Nori:
Semi-Supervised Learning via Compact Latent Space Clustering. ICML 2018: 2464-2473 - [c7]Amir Alansary, Loïc Le Folgoc, Ghislain Vaillant, Ozan Oktay, Yuanwei Li, Wenjia Bai, Jonathan Passerat-Palmbach, Ricardo Guerrero, Konstantinos Kamnitsas, Benjamin Hou, Steven G. McDonagh, Ben Glocker, Bernhard Kainz, Daniel Rueckert:
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents. MICCAI (1) 2018: 277-285 - [i3]Ozan Oktay, Jo Schlemper, Loïc Le Folgoc, Matthew C. H. Lee, Mattias P. Heinrich, Kazunari Misawa, Kensaku Mori, Steven G. McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert:
Attention U-Net: Learning Where to Look for the Pancreas. CoRR abs/1804.03999 (2018) - [i2]Konstantinos Kamnitsas, Daniel Coelho de Castro, Loïc Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya V. Nori:
Semi-Supervised Learning via Compact Latent Space Clustering. CoRR abs/1806.02679 (2018) - [i1]Amir Alansary, Loïc Le Folgoc, Ghislain Vaillant, Ozan Oktay, Yuanwei Li, Wenjia Bai, Jonathan Passerat-Palmbach, Ricardo Guerrero, Konstantinos Kamnitsas, Benjamin Hou, Steven G. McDonagh, Ben Glocker, Bernhard Kainz, Daniel Rueckert:
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents. CoRR abs/1806.03228 (2018) - 2017
- [j2]Loïc Le Folgoc, Hervé Delingette, Antonio Criminisi, Nicholas Ayache:
Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements. Medical Image Anal. 36: 79-97 (2017) - [j1]Loïc Le Folgoc, Hervé Delingette, Antonio Criminisi, Nicholas Ayache:
Quantifying Registration Uncertainty With Sparse Bayesian Modelling. IEEE Trans. Medical Imaging 36(2): 607-617 (2017) - [c6]Loïc Le Folgoc, Aditya V. Nori, Antonio Criminisi:
Spectral Kernels for Probabilistic Analysis and Clustering of Shapes. IPMI 2017: 67-79 - 2016
- [c5]Loïc Le Folgoc, Aditya V. Nori, Siddharth Ancha, Antonio Criminisi:
Lifted Auto-Context Forests for Brain Tumour Segmentation. BrainLes@MICCAI 2016: 171-183 - 2015
- [b1]Loïc Le Folgoc:
Statistical learning for image-based personalization of cardiac models. (Apprentissage statistique pour la personnalisation de modèles cardiaques à partir de données d'imagerie). University of Nice Sophia Antipolis, France, 2015 - 2014
- [c4]Sophie Giffard-Roisin, Stéphanie Marchesseau, Loïc Le Folgoc, Hervé Delingette, Maxime Sermesant:
Evaluation of Personalised Canine Electromechanical Models. STACOM 2014: 74-82 - [c3]Rocío Cabrera Lozoya, Ján Margeta, Loïc Le Folgoc, Yuki Komatsu, Benjamin Berte, Jatin Relan, Hubert Cochet, Michel Haïssaguerre, Pierre Jaïs, Nicholas Ayache, Maxime Sermesant:
Confidence-Based Training for Clinical Data Uncertainty in Image-Based Prediction of Cardiac Ablation Targets. MCV 2014: 148-159 - [c2]Loïc Le Folgoc, Hervé Delingette, Antonio Criminisi, Nicholas Ayache:
Sparse Bayesian Registration. MICCAI (1) 2014: 235-242 - 2012
- [c1]Loïc Le Folgoc, Hervé Delingette, Antonio Criminisi, Nicholas Ayache:
Current-Based 4D Shape Analysis for the Mechanical Personalization of Heart Models. MCV 2012: 283-292
Coauthor Index
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