L
Loic Le Folgoc
Researcher at Imperial College London
Publications - 41
Citations - 3019
Loic Le Folgoc is an academic researcher from Imperial College London. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 12, co-authored 39 publications receiving 1587 citations. Previous affiliations of Loic Le Folgoc include Microsoft & French Institute for Research in Computer Science and Automation.
Papers
More filters
Posted Content
Attention U-Net: Learning Where to Look for the Pancreas
Ozan Oktay,Jo Schlemper,Loic Le Folgoc,Matthew C. H. Lee,Mattias P. Heinrich,Kazunari Misawa,Kensaku Mori,Steven McDonagh,Nils Y. Hammerla,Bernhard Kainz,Ben Glocker,Daniel Rueckert +11 more
TL;DR: A novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes is proposed to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs).
Journal ArticleDOI
Evaluating reinforcement learning agents for anatomical landmark detection.
Amir Alansary,Ozan Oktay,Yuanwei Li,Loic Le Folgoc,Benjamin Hou,Ghislain Vaillant,Konstantinos Kamnitsas,Athanasios Vlontzos,Ben Glocker,Bernhard Kainz,Daniel Rueckert +10 more
TL;DR: Novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans are evaluated and the performance of these agents surpasses state‐of‐the‐art supervised and RL methods.
Posted Content
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas,Daniel Coelho de Castro,Loic Le Folgoc,Ian Walker,Ryutaro Tanno,Daniel Rueckert,Ben Glocker,Antonio Criminisi,Aditya V. Nori +8 more
TL;DR: In this article, a cost function based on Markov chains on the graph is proposed to regularize the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization.
Book ChapterDOI
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Amir Alansary,Loic Le Folgoc,Ghislain Vaillant,Ozan Oktay,Yuanwei Li,Wenjia Bai,Jonathan Passerat-Palmbach,Ricardo Guerrero,Konstantinos Kamnitsas,Benjamin Hou,Steven McDonagh,Ben Glocker,Bernhard Kainz,Daniel Rueckert +13 more
TL;DR: In this article, a multi-scale RL agent framework was employed to find standardized view planes in 3D image acquisitions, which can be used to mimic experienced operators and achieve an accuracy of 1.53 mm, 1.98 mm and 4.84 mm.
Journal ArticleDOI
Quantifying Registration Uncertainty With Sparse Bayesian Modelling.
TL;DR: The true posterior distribution under the sparse Bayesian model is found to be meaningful: orders of magnitude for the estimated uncertainty are quantitatively reasonable, the uncertainty is higher in textureless regions and lower in the direction of strong intensity gradients.