no code implementations • 22 Mar 2024 • Vladyslav Zalevskyi, Thomas Sanchez, Margaux Roulet, Jordina Aviles Verddera, Jana Hutter, Hamza Kebiri, Meritxell Bach Cuadra
Segmentation of fetal brain tissue from magnetic resonance imaging (MRI) plays a crucial role in the study of in utero neurodevelopment.
no code implementations • 7 Dec 2023 • Thomas Sanchez
Altogether, our findings suggest that stochastic LBCS and similar methods represent promising alternatives to deep RL.
1 code implementation • 8 Nov 2023 • Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, Meritxell Bach Cuadra
We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data.
1 code implementation • 12 Apr 2023 • Thomas Sanchez, Oscar Esteban, Yvan Gomez, Elisenda Eixarch, Meritxell Bach Cuadra
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies.
no code implementations • 25 Nov 2022 • Priscille de Dumast, Thomas Sanchez, Hélène Lajous, Meritxell Bach Cuadra
Tuning the regularization hyperparameter $\alpha$ in inverse problems has been a longstanding problem.
no code implementations • 29 Sep 2021 • Thomas Sanchez, Igor Krawczuk, Volkan Cevher
Deep learning approaches have shown great promise in accelerating magnetic resonance imaging (MRI), by reconstructing high quality images from highly undersampled data.
1 code implementation • 3 Nov 2020 • Zhaodong Sun, Thomas Sanchez, Fabian Latorre, Volkan Cevher
When the noise level is small, it does not considerably reduce the overfitting problem.
no code implementations • 23 Oct 2020 • Thomas Sanchez, Igor Krawczuk, Zhaodong Sun, Volkan Cevher
We propose an adaptive sampling method for the linear model, driven by the uncertainty estimation with a generative adversarial network (GAN) model.
no code implementations • 25 Sep 2019 • Thomas Sanchez, Igor Krawczuk, Zhaodong Sun, Volkan Cevher
This work proposes a closed-loop, uncertainty-driven adaptive sampling frame- work (CLUDAS) for accelerating magnetic resonance imaging (MRI) via deep Bayesian inversion.
1 code implementation • 1 Feb 2019 • Thomas Sanchez, Baran Gözcü, Ruud B. van Heeswijk, Armin Eftekhari, Efe Ilıcak, Tolga Çukur, Volkan Cevher
Compressed sensing applied to magnetic resonance imaging (MRI) allows to reduce the scanning time by enabling images to be reconstructed from highly undersampled data.