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pro vyhledávání: '"Painchaud, Nathan"'
Autor:
Painchaud, Nathan, Stym-Popper, Jérémie, Courand, Pierre-Yves, Thome, Nicolas, Jodoin, Pierre-Marc, Duchateau, Nicolas, Bernard, Olivier
Deep learning enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain. These descriptors provide fine-grained information that physicians consider, in conjunction w
Externí odkaz:
http://arxiv.org/abs/2401.07796
Autor:
Ling, Hang Jung, Painchaud, Nathan, Courand, Pierre-Yves, Jodoin, Pierre-Marc, Garcia, Damien, Bernard, Olivier
Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases). However, the
Externí odkaz:
http://arxiv.org/abs/2305.01997
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached,
Externí odkaz:
http://arxiv.org/abs/2112.02102
Autor:
Armenta, Marco, Judge, Thierry, Painchaud, Nathan, Skandarani, Youssef, Lemaire, Carl, Sanchez, Gabriel Gibeau, Spino, Philippe, Jodoin, Pierre-Marc
In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks. Neural teleportation "teleports" a network to a new position in the weight space and preserves it
Externí odkaz:
http://arxiv.org/abs/2012.01118
Autor:
Painchaud, Nathan, Skandarani, Youssef, Judge, Thierry, Bernard, Olivier, Lalande, Alain, Jodoin, Pierre-Marc
Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation results are closer than ever to the inter-expert variability, CNNs ar
Externí odkaz:
http://arxiv.org/abs/2006.08825
In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate groundtruth for the application of cine-MR image cardiac segmentation. On on
Externí odkaz:
http://arxiv.org/abs/2005.09026
Autor:
Painchaud, Nathan, Skandarani, Youssef, Judge, Thierry, Bernard, Olivier, Lalande, Alain, Jodoin, Pierre-Marc
Publikováno v:
in Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019, pp. 632-640
Recent publications have shown that the segmentation accuracy of modern-day convolutional neural networks (CNN) applied on cardiac MRI can reach the inter-expert variability, a great achievement in this area of research. However, despite these succes
Externí odkaz:
http://arxiv.org/abs/1907.02865
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Akademický článek
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Publikováno v:
IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging, 2022, 41 (10), pp.2867-2878. ⟨10.1109/TMI.2022.3173669⟩
IEEE Transactions on Medical Imaging, 2022, 41 (10), pp.2867-2878. ⟨10.1109/TMI.2022.3173669⟩
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached,