Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Santini, Gianmarco"'
Autor:
Fourcade, Constance, Ferrer, Ludovic, Moreau, Noemie, Santini, Gianmarco, Brennan, Aishlinn, Rousseau, Caroline, Lacombe, Marie, Fleury, Vincent, Colombié, Mathilde, Jézéquel, Pascal, Campone, Mario, Rubeaux, Mathieu, Mateus, Diana
Longitudinal image registration is challenging and has not yet benefited from major performance improvements thanks to deep-learning. Inspired by Deep Image Prior, this paper introduces a different use of deep architectures as regularizers to tackle
Externí odkaz:
http://arxiv.org/abs/2111.11873
Precise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed. The
Externí odkaz:
http://arxiv.org/abs/1909.00735
Autor:
Martini, Nicola, Vatti, Alessio, Ripoli, Andrea, Salaris, Sara, Santini, Gianmarco, Santarelli, Maria Filomena, Chiappino, Dante, Della Latta, Daniele
Cardiac magnetic resonance parametric T1 maps are typically reconstructed using non-linear fitting. However this method has limitations due to the high computational cost and robustness. In this study, a recurrent neural network (RNN) is proposed for
Externí odkaz:
http://arxiv.org/abs/1907.12454
Autor:
Valvano, Gabriele, Leo, Andrea, Della Latta, Daniele, Martini, Nicola, Santini, Gianmarco, Chiappino, Dante, Ricciardi, Emiliano
Recent research put a big effort in the development of deep learning architectures and optimizers obtaining impressive results in areas ranging from vision to language processing. However little attention has been addressed to the need of a methodolo
Externí odkaz:
http://arxiv.org/abs/1810.12142
Autor:
Valvano, Gabriele, Martini, Nicola, Leo, Andrea, Santini, Gianmarco, Della Latta, Daniele, Ricciardi, Emiliano, Chiappino, Dante
Skull-stripping methods aim to remove the non-brain tissue from acquisition of brain scans in magnetic resonance (MR) imaging. Although several methods sharing this common purpose have been presented in literature, they all suffer from the great vari
Externí odkaz:
http://arxiv.org/abs/1810.10853
Autor:
Valvano, Gabriele, Santini, Gianmarco, Martini, Nicola, Ripoli, Andrea, Iacconi, Chiara, Chiappino, Dante, Della Latta, Daniele
Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work we used 283 mammogr
Externí odkaz:
http://arxiv.org/abs/1809.03788
Autor:
Santini, Gianmarco, Zumbo, Lorena M., Martini, Nicola, Valvano, Gabriele, Leo, Andrea, Ripoli, Andrea, Avogliero, Francesco, Chiappino, Dante, Della Latta, Daniele
In Europe the 20% of the CT scans cover the thoracic region. The acquired images contain information about the cardiovascular system that often remains latent due to the lack of contrast in the cardiac area. On the other hand, the contrast enhanced c
Externí odkaz:
http://arxiv.org/abs/1807.01779
Akademický článek
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Akademický článek
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Autor:
Fourcade, Constance, Santini, Gianmarco, Ferrer, Ludovic, Rousseau, Caroline, Colombié, Mathilde, Campone, Mario, Rubeaux, Mathieu, Mateus, Diana
Publikováno v:
NTHS-Nuclear Technology for Health Symposium
NTHS-Nuclear Technology for Health Symposium, Feb 2020, Nantes, France
NTHS-Nuclear Technology for Health Symposium, Feb 2020, Nantes, France
National audience; HypothesisIn the clinical follow-up of metastatic breast cancer patients, semi-automatic measurements are performed on 18FDG PET/CT images to monitor the evolution of the main metastatic sites. Apart from being time-consuming and p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::4fb8ddb4d7e2fa28e9d96300a5296fb6
https://hal.archives-ouvertes.fr/hal-02565107
https://hal.archives-ouvertes.fr/hal-02565107