Zobrazeno 1 - 10
of 197
pro vyhledávání: '"Decazes, Pierre"'
Autor:
Mesbah, Zacharia, Mottay, Léo, Modzelewski, Romain, Decazes, Pierre, Hapdey, Sébastien, Ruan, Su, Thureau, Sébastien
For the last three years, the AutoPET competition gathered the medical imaging community around a hot topic: lesion segmentation on Positron Emitting Tomography (PET) scans. Each year a different aspect of the problem is presented; in 2024 the multip
Externí odkaz:
http://arxiv.org/abs/2410.02807
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis. For this reason, physicians generally diagnose diseases based on multimodal medical images such as, e.g., PET/CT. The effective fu
Externí odkaz:
http://arxiv.org/abs/2309.05919
Publikováno v:
In Information Fusion January 2025 113
Publikováno v:
International Journal of Approximate Reasoning, Volume 149, 2022, Pages 39-60
An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (PET) and Computed Tomography (CT) images. The architecture is composed
Externí odkaz:
http://arxiv.org/abs/2201.13078
Autor:
Yousefirizi, Fereshteh, Decazes, Pierre, Amyar, Amine, Ruan, Su, Saboury, Babak, Rahmim, Arman
Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns. AI-based detection searches the image space to find the regions of interest ba
Externí odkaz:
http://arxiv.org/abs/2110.10332
Autor:
Botsen, Damien, Chabaud, Sylvie, Perrier, Hervé, Ammarguellat, Hanifa, Jestin-Le-Tallec, Véronique, Olesinski, Jonathan, Toullec, Clémence, Aparicio, Thomas, Ben Abdelghani, Meher, Borg, Christophe, Bouche, Olivier, Coutzac, Clélia, Devaud, Hervé, Di Fiore, Frédéric, Dubreuil, Olivier, Evesque, Ludovic, Huguenin, Bruno, Muller, Marie, Poureau, Pierre-Guillaume, Oularue, Emilie, Tougeron, David, Zaanan, Aziz, Ammari, Samy, De Sousa Carvalho, Nicolas, Decazes, Pierre, De La Fouchardiere, Christelle
Publikováno v:
In Digestive and Liver Disease August 2024 56(8):1281-1287
Lymphoma detection and segmentation from whole-body Positron Emission Tomography/Computed Tomography (PET/CT) volumes are crucial for surgical indication and radiotherapy. Designing automatic segmentation methods capable of effectively exploiting the
Externí odkaz:
http://arxiv.org/abs/2108.05422
PET and CT are two modalities widely used in medical image analysis. Accurately detecting and segmenting lymphomas from these two imaging modalities are critical tasks for cancer staging and radiotherapy planning. However, this task is still challeng
Externí odkaz:
http://arxiv.org/abs/2104.13293
Autor:
Camus, Vincent, Viennot, Mathieu, Viailly, Pierre-Julien, Drieux, Fanny, Veresezan, Elena-Liana, Bobée, Victor, Rainville, Vinciane, Bohers, Elodie, Sesques, Pierre, Haioun, Corinne, Durot, Eric, Bayaram, Michael, Rossi, Cédric, Martin, Laurent, Penther, Dominique, Kaltenbach, Sophie, Bruneau, Julie, Paillassa, Jérôme, Tournilhac, Olivier, Gower, Nicolas, Willaume, Alexandre, Antier, Chloé, Renaud, Loïc, Lévêque, Emilie, Decazes, Pierre, Becker, Stéphanie, Tonnelet, David, Gaulard, Philippe, Tilly, Hervé, Molina, Thierry Jo, Traverse-Glehen, Alexandra, Donzel, Marie, Ruminy, Philippe, Jardin, Fabrice
Publikováno v:
In Blood Advances September 2024
One of the most challenges in medical imaging is the lack of data. It is proven that classical data augmentation methods are useful but still limited due to the huge variation in images. Using generative adversarial networks (GAN) is a promising way
Externí odkaz:
http://arxiv.org/abs/2003.08663