Current-Based 4D Shape Analysis for the Mechanical Personalization of Heart Models. - Loïc Le Folgoc; Hervé Delingette; Antonio Criminisi; Nicholas Ayache (MCV, 2012)

Sparse Bayesian Registration. - Loïc Le Folgoc; Hervé Delingette; Antonio Criminisi; Nicholas Ayache (MICCAI (1), 2014)

Evaluation of Personalised Canine Electromechanical Models. - Sophie Giffard-Roisin; Stéphanie Marchesseau; Loïc Le Folgoc; Hervé Delingette; Maxime Sermesant (STACOM, 2014)

Confidence-Based Training for Clinical Data Uncertainty in Image-Based Prediction of Cardiac Ablation Targets. - Rocío Cabrera Lozoya; Ján Margeta; Loïc Le Folgoc; Yuki Komatsu; Benjamin Berte; Jatin Relan; Hubert Cochet; Michel Haïssaguerre; Pierre Jaïs; Nicholas Ayache; Maxime Sermesant (MCV, 2014)

Statistical learning for image-based personalization of cardiac models. (Apprentissage statistique pour la personnalisation de modèles cardiaques à partir de données d'imagerie). - Loïc Le Folgoc (2015)

Lifted Auto-Context Forests for Brain Tumour Segmentation. - Loïc Le Folgoc; Aditya V. Nori; Siddharth Ancha; Antonio Criminisi (BrainLes@MICCAI, 2016)

Spectral Kernels for Probabilistic Analysis and Clustering of Shapes. - Loïc Le Folgoc; Aditya V. Nori; Antonio Criminisi (IPMI, 2017)

Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements. - Loïc Le Folgoc; Hervé Delingette; Antonio Criminisi; Nicholas Ayache (Medical Image Anal., 2017)

Quantifying Registration Uncertainty With Sparse Bayesian Modelling. - Loïc Le Folgoc; Hervé Delingette; Antonio Criminisi; Nicholas Ayache (IEEE Trans. Medical Imaging, 2017)

Semi-Supervised Learning via Compact Latent Space Clustering. - Konstantinos Kamnitsas; Daniel Coelho de Castro; Loïc Le Folgoc; Ian Walker; Ryutaro Tanno; Daniel Rueckert; Ben Glocker; Antonio Criminisi; Aditya V. Nori (ICML, 2018)

Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents. - Amir Alansary; Loïc Le Folgoc; Ghislain Vaillant; Ozan Oktay; Yuanwei Li; Wenjia Bai; Jonathan Passerat-Palmbach; Ricardo Guerrero; Konstantinos Kamnitsas; Benjamin Hou; Steven McDonagh 001; Ben Glocker; et al. (MICCAI (1), 2018)

Attention U-Net: Learning Where to Look for the Pancreas. - Ozan Oktay; Jo Schlemper; Loïc Le Folgoc; Matthew C. H. Lee; Mattias P. Heinrich; Kazunari Misawa; Kensaku Mori; Steven G. McDonagh 001; Nils Y. Hammerla; Bernhard Kainz; Ben Glocker; Daniel Rueckert (CoRR, 2018)

Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing. - Loïc Le Folgoc; Daniel Coelho de Castro; Jeremy Tan; Bishesh Khanal; Konstantinos Kamnitsas; Ian Walker; Amir Alansary; Ben Glocker (IPMI, 2019)

Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training. - Huaqi Qiu; Chen Qin; Loïc Le Folgoc; Benjamin Hou; Jo Schlemper; Daniel Rueckert (STACOM@MICCAI, 2019)

Explainable Shape Analysis through Deep Hierarchical Generative Models: Application to Cardiac Remodeling. - Carlo Biffi; Juan J. Cerrolaza; Giacomo Tarroni; Wenjia Bai; Ozan Oktay; Loïc Le Folgoc; Konstantinos Kamnitsas; Antonio de Marvao; Georgia Doumou; Jinming Duan 001; Sanjay K. Prasad; Stuart A. Cook; et al. (CoRR, 2019)

Evaluating reinforcement learning agents for anatomical landmark detection. - Amir Alansary; Ozan Oktay; Yuanwei Li; Loïc Le Folgoc; Benjamin Hou; Ghislain Vaillant; Konstantinos Kamnitsas; Athanasios Vlontzos; Ben Glocker; Bernhard Kainz; Daniel Rueckert (Medical Image Anal., 2019)

Image Registration via Stochastic Gradient Markov Chain Monte Carlo. - Daniel Grzech; Bernhard Kainz; Ben Glocker; Loïc Le Folgoc (UNSURE/GRAIL@MICCAI, 2020)

Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface. - Vitalis Vosylius; Andy Wang; Cemlyn Waters; Alexey Zakharov; Francis Ward; Loïc Le Folgoc; John Cupitt; Antonios Makropoulos; Andreas Schuh; Daniel Rueckert; Amir Alansary (UNSURE/GRAIL@MICCAI, 2020)

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty. - Miguel Monteiro; Loïc Le Folgoc; Daniel Coelho de Castro; Nick Pawlowski; Bernardo Marques; Konstantinos Kamnitsas; Mark van der Wilk; Ben Glocker (NeurIPS, 2020)

Bayesian Sampling Bias Correction: Training with the Right Loss Function. - 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 (CoRR, 2020)

Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models. - 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 001; Sanjay K. Prasad; et al. (IEEE Trans. Medical Imaging, 2020)

Patient-Specific 3d Cellular Automata Nodule Growth Synthesis In Lung Cancer Without The Need Of External Data. - Octavio E. Martinez Manzanera; Sam Ellis; Vasileios Baltatzis; Arjun Nair; Loïc Le Folgoc; Sujal Desai; Ben Glocker; Julia A. Schnabel (ISBI, 2021)

The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification. - Vasileios Baltatzis; Kyriaki-Margarita Bintsi; Loïc Le Folgoc; Octavio E. Martinez Manzanera; Sam Ellis; Arjun Nair; Sujal Desai; Ben Glocker; Julia A. Schnabel (PRIME@MICCAI, 2021)

The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data. - Vasileios Baltatzis; Loïc Le Folgoc; Sam Ellis; Octavio E. Martinez Manzanera; Kyriaki-Margarita Bintsi; Arjun Nair; Sujal Desai; Ben Glocker; Julia A. Schnabel (iMIMIC/TDA4MedicalData@MICCAI, 2021)

Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data. - 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 (CoRR, 2021)

Is MC Dropout Bayesian? - Loïc Le Folgoc; Vasileios Baltatzis; Sujal Desai; Anand Devaraj; Sam Ellis; Octavio E. Martinez Manzanera; Arjun Nair; Huaqi Qiu; Julia A. Schnabel; Ben Glocker (CoRR, 2021)

Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo. - Daniel Grzech; Mohammad Farid Azampour; Huaqi Qiu; Ben Glocker; Bernhard Kainz; Loïc Le Folgoc (CoRR, 2021)

A variational Bayesian method for similarity learning in non-rigid image registration. - Daniel Grzech; Mohammad Farid Azampour; Ben Glocker; Julia A. Schnabel; Nassir Navab; Bernhard Kainz; Loïc Le Folgoc (CVPR, 2022)

Dense Self-Supervised Learning for Medical Image Segmentation. - Maxime Seince; Loïc Le Folgoc; Luiz Augusto Facury de Souza; Elsa D. Angelini (MIDL, 2024)

Unsupervised Similarity Learning for Image Registration with Energy-Based Models. - Daniel Grzech; Loïc Le Folgoc; Mohammad Farid Azampour; Athanasios Vlontzos; Ben Glocker; Nassir Navab; Julia A. Schnabel; Bernhard Kainz (WBIR, 2024)

Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study. - Ali Mammadov; Loïc Le Folgoc; Julien Adam; Anne Buronfosse; Gilles Hayem; Guillaume Hocquet; Pietro Gori (CoRR, 2025)

Reducing Variability of Multiple Instance Learning Methods for Digital Pathology. - Ali Mammadov; Loïc Le Folgoc; Guillaume Hocquet; Pietro Gori (CoRR, 2025)