The Experts below are selected from a list of 15990633 Experts worldwide ranked by ideXlab platform
Joachim M. Buhmann - One of the best experts on this subject based on the ideXlab platform.
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AIME - Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.
Artificial Intelligence in Medicine, 2019Co-Authors: Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. BuhmannAbstract:Mitral valve segmentation specifies a crucial first step to establish a machine learning pipeline that can support practitioners into performing diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a low-dimensional neural network matrix factorization of echocardiography videos. The Method is evaluated in a collection of echocardiography videos of patients with a variety of mitral valve diseases and exceeds the State-of-the-Art Method in all the metrics considered.
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Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.
EasyChair Preprints, 2019Co-Authors: Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. BuhmannAbstract:Mitral valve segmentation is a crucial first step to establish a machine learning pipeline that can support practitioners into performing the diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a neural network low-dimension matrix factorization of the echocardiography video. The Method is evaluated in a collection of echocardiography video of patients with a variety of mitral valve diseases and exceeds the State-of-the-Art Method in all the metrics considered.
Luca Corinzia - One of the best experts on this subject based on the ideXlab platform.
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AIME - Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.
Artificial Intelligence in Medicine, 2019Co-Authors: Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. BuhmannAbstract:Mitral valve segmentation specifies a crucial first step to establish a machine learning pipeline that can support practitioners into performing diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a low-dimensional neural network matrix factorization of echocardiography videos. The Method is evaluated in a collection of echocardiography videos of patients with a variety of mitral valve diseases and exceeds the State-of-the-Art Method in all the metrics considered.
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Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.
EasyChair Preprints, 2019Co-Authors: Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. BuhmannAbstract:Mitral valve segmentation is a crucial first step to establish a machine learning pipeline that can support practitioners into performing the diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a neural network low-dimension matrix factorization of the echocardiography video. The Method is evaluated in a collection of echocardiography video of patients with a variety of mitral valve diseases and exceeds the State-of-the-Art Method in all the metrics considered.
Bernard Legras - One of the best experts on this subject based on the ideXlab platform.
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Sparse analysis for mesoscale convective systems tracking
Signal Processing: Image Communication, 2020Co-Authors: Jean-baptiste Courbot, Vincent Duval, Bernard LegrasAbstract:In this paper, we study the tracking of de-formable shapes in sequences of images. Our target application is the tracking of clouds in satellite image. We propose to use a recent State-of-the-Art Method for off-the-grid sparse analysis to describe clouds in image as mixtures of 2D atoms. Then, we introduce an algorithm to handle the tracking with its specificities: apparition or disappearance of objects, merging, and splitting. This Method provides similar numerical outputs as the recent State-of-the-Art alternatives, while being more flexible, and providing additional information on, e.g., cloud surface brightness.
Maurizio Tamarasso - One of the best experts on this subject based on the ideXlab platform.
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AIME - Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.
Artificial Intelligence in Medicine, 2019Co-Authors: Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. BuhmannAbstract:Mitral valve segmentation specifies a crucial first step to establish a machine learning pipeline that can support practitioners into performing diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a low-dimensional neural network matrix factorization of echocardiography videos. The Method is evaluated in a collection of echocardiography videos of patients with a variety of mitral valve diseases and exceeds the State-of-the-Art Method in all the metrics considered.
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Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.
EasyChair Preprints, 2019Co-Authors: Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. BuhmannAbstract:Mitral valve segmentation is a crucial first step to establish a machine learning pipeline that can support practitioners into performing the diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a neural network low-dimension matrix factorization of the echocardiography video. The Method is evaluated in a collection of echocardiography video of patients with a variety of mitral valve diseases and exceeds the State-of-the-Art Method in all the metrics considered.
Jesse Provost - One of the best experts on this subject based on the ideXlab platform.
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AIME - Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.
Artificial Intelligence in Medicine, 2019Co-Authors: Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. BuhmannAbstract:Mitral valve segmentation specifies a crucial first step to establish a machine learning pipeline that can support practitioners into performing diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a low-dimensional neural network matrix factorization of echocardiography videos. The Method is evaluated in a collection of echocardiography videos of patients with a variety of mitral valve diseases and exceeds the State-of-the-Art Method in all the metrics considered.
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Unsupervised mitral valve segmentation in echocardiography with neural network matrix factorization.
EasyChair Preprints, 2019Co-Authors: Luca Corinzia, Jesse Provost, Alessandro Candreva, Maurizio Tamarasso, Francesco Maisano, Joachim M. BuhmannAbstract:Mitral valve segmentation is a crucial first step to establish a machine learning pipeline that can support practitioners into performing the diagnosis of mitral valve diseases, surgical planning, and intraoperative procedures. To this end, we propose a totally automated and unsupervised mitral valve segmentation algorithm, based on a neural network low-dimension matrix factorization of the echocardiography video. The Method is evaluated in a collection of echocardiography video of patients with a variety of mitral valve diseases and exceeds the State-of-the-Art Method in all the metrics considered.
