The challenge of mapping the human connectome based on diffusion tractography KH Maier-Hein, PF Neher, JC Houde, MA Côté, E Garyfallidis, J Zhong, ... Nature communications 8 (1), 1349, 2017 | 1270 | 2017 |
Learning a probabilistic model for diffeomorphic registration J Krebs, H Delingette, B Mailhé, N Ayache, T Mansi IEEE transactions on medical imaging 38 (9), 2165-2176, 2019 | 244 | 2019 |
Shift-invariant dictionary learning for sparse representations: extending K-SVD B Mailhé, S Lesage, R Gribonval, F Bimbot, P Vandergheynst 2008 16th European Signal Processing Conference, 1-5, 2008 | 126 | 2008 |
Unsupervised probabilistic deformation modeling for robust diffeomorphic registration J Krebs, T Mansi, B Mailhé, N Ayache, H Delingette Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2018 | 100 | 2018 |
Image correction using a deep generative machine-learning model B Mailhe, HE Cetingul, BL Odry, X Chen, MS Nadar US Patent 10,387,765, 2019 | 79 | 2019 |
AIR-MRF: accelerated iterative reconstruction for magnetic resonance fingerprinting CC Cline, X Chen, B Mailhe, Q Wang, J Pfeuffer, M Nittka, MA Griswold, ... Magnetic resonance imaging 41, 29-40, 2017 | 73 | 2017 |
Tractography-based connectomes are dominated by false-positive connections KH Maier-Hein, P Neher, JC Houde, MA Côté, E Garyfallidis, J Zhong, ... BioRxiv, 084137, 2016 | 72 | 2016 |
INK-SVD: Learning incoherent dictionaries for sparse representations B Mailhé, D Barchiesi, MD Plumbley 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012 | 71 | 2012 |
Learning multimodal dictionaries G Monaci, P Jost, P Vandergheynst, B Mailhe, S Lesage, R Gribonval IEEE Transactions on Image Processing 16 (9), 2272-2283, 2007 | 64 | 2007 |
A low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries B Mailhé, R Gribonval, F Bimbot, P Vandergheynst 2009 IEEE International Conference on Acoustics, Speech and Signal …, 2009 | 58 | 2009 |
Image-based tumor phenotyping with machine learning from synthetic data D Comaniciu, A Kamen, D Liu, B Mailhe, T Mansi US Patent 10,282,588, 2019 | 48 | 2019 |
Denoising medical images by learning sparse image representations with a deep unfolding approach K Mentl, B Mailhe, MS Nadar US Patent 10,685,429, 2020 | 40 | 2020 |
Image quality score using a deep generative machine-learning model BL Odry, B Mailhe, HE Cetingul, X Chen, MS Nadar US Patent 10,043,088, 2018 | 37 | 2018 |
Learning-based framework for personalized image quality evaluation and optimization B Mailhe, MS Nadar US Patent 9,916,525, 2018 | 37 | 2018 |
Fast orthogonal sparse approximation algorithms over local dictionaries B Mailhé, R Gribonval, P Vandergheynst, F Bimbot Signal Processing 91 (12), 2822-2835, 2011 | 36 | 2011 |
Magnetic resonance image reconstruction with deep reinforcement learning B Mailhe, BL Odry, X Chen, MS Nadar US Patent 10,573,031, 2020 | 35 | 2020 |
The challenge of mapping the human connectome based on diffusion tractography. Nat Commun 8: 1349 KH Maier-Hein, PF Neher, JC Houde, MA Côté, E Garyfallidis, J Zhong, ... | 35 | 2017 |
Dictionary learning for the sparse modelling of atrial fibrillation in ECG signals B Mailhé, R Gribonval, F Bimbot, M Lemay, P Vandergheynst, JM Vesin 2009 IEEE International Conference on Acoustics, Speech and Signal …, 2009 | 35 | 2009 |
High‐resolution dynamic CE‐MRA of the thorax enabled by iterative TWIST reconstruction J Wetzl, C Forman, BJ Wintersperger, L D'Errico, M Schmidt, B Mailhe, ... Magnetic resonance in medicine 77 (2), 833-840, 2017 | 33 | 2017 |
Automated detection and quantification of COVID-19 airspace disease on chest radiographs: a novel approach achieving expert radiologist-level performance using a deep … EJM Barbosa Jr, WB Gefter, FC Ghesu, S Liu, B Mailhe, A Mansoor, ... Investigative radiology 56 (8), 471-479, 2021 | 32 | 2021 |