Olivier Humbert

According to our database, Olivier Humbert authored at least 11 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications.

Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago Silva, Samy-Safwan Ayed, Riccardo Taiello, Laetitia Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek Önen, Marco Lorenzi

CoRR, 2023

Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism.

Aude Sportisse, Hugo Schmutz, Olivier Humbert, Charles Bouveyron, Pierre-Alexandre Mattei

Proceedings of the International Conference on Machine Learning, 2023

Don't fear the unlabelled: safe semi-supervised learning via debiasing.

Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Don't fear the unlabelled: safe deep semi-supervised learning via simple debiasing.

Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei

CoRR, 2022

Learning a confidence score and the latent space of a new supervised autoencoder for diagnosis and prognosis in clinical metabolomic studies.

David Chardin, Cyprien Gille, Thierry Pourcher, Olivier Humbert, Michel Barlaud

BMC Bioinform., 2022

Privacy Preserving Image Registration.

Riccardo Taiello, Melek Önen, Olivier Humbert, Marco Lorenzi

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

2021

Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection - an application in metabolomics studies.

David Chardin, Olivier Humbert, Caroline Bailleux, Fanny Burel-Vandenbos, Valerie Rigau, Thierry Pourcher, Michel Barlaud

BMC Bioinform., 2021

2018

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard, Alain Lalande, Clément Zotti, Frederic Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Ángel González Ballester, Gerard Sanroma, Sandy Napel, Steffen E. Petersen, Georgios Tziritas, Elias Grinias, Mahendra Khened, Alex Varghese, Ganapathy Krishnamurthi, Marc-Michel Rohé, Xavier Pennec, Maxime Sermesant, Fabian Isensee, Paul Jaeger, Klaus H. Maier-Hein, Peter M. Full, Ivo Wolf, Sandy Engelhardt, Christian F. Baumgartner, Lisa M. Koch, Jelmer M. Wolterink, Ivana Isgum, Yeonggul Jang, Yoonmi Hong, Jay Patravali, Shubham Jain, Olivier Humbert, Pierre-Marc Jodoin

IEEE Trans. Medical Imaging, 2018

2017

Novel Deep Convolution Neural Network Applied to MRI Cardiac Segmentation.

Clément Zotti, Zhiming Luo, Alain Lalande, Olivier Humbert, Pierre-Marc Jodoin

CoRR, 2017

Graph cut-based method for segmenting the left ventricle from MRI or echocardiographic images.

Michaël Bernier, Pierre-Marc Jodoin, Olivier Humbert, Alain Lalande

Comput. Medical Imaging Graph., 2017

GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation.

Clément Zotti, Zhiming Luo, Olivier Humbert, Alain Lalande, Pierre-Marc Jodoin

Proceedings of the Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, 2017