Zobrazeno 1 - 10
of 385
pro vyhledávání: '"Ranazzi A"'
Autor:
Luo, Xiaodong, Ranazzi, Paulo
Publikováno v:
In Geoenergy Science and Engineering December 2024 243
Publikováno v:
In Computers and Geosciences November 2024 193
Autor:
Pasquini, Luca, Napolitano, Antonio, Tagliente, Emanuela, Dellepiane, Francesco, Lucignani, Martina, Vidiri, Antonello, Ranazzi, Giulio, Stoppacciaro, Antonella, Moltoni, Giulia, Nicolai, Matteo, Romano, Andrea, Di Napoli, Alberto, Bozzao, Alessandro
Background: Distinction of IDH mutant and wildtype GBMs is challenging on MRI, since conventional imaging shows considerable overlap. While few studies employed deep-learning in a mixed low/high grade glioma population, a GBM-specific model is still
Externí odkaz:
http://arxiv.org/abs/2102.13205
Autor:
Pasquini, Luca, Napolitano, Antonio, Lucignani, Martina, Tagliente, Emanuela, Dellepiane, Francesco, Rossi-Espagnet, Maria Camilla, Ritrovato, Matteo, Vidiri, Antonello, Villani, Veronica, Ranazzi, Giulio, Stoppacciaro, Antonella, Romano, Andrea, Di Napoli, Alberto, Bozzao, Alessandro
Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM). However, clinical implementation is limited by lack of parameters standardization. We aimed to compare nine machine learning classifiers, with d
Externí odkaz:
http://arxiv.org/abs/2102.06526
Publikováno v:
In Journal of Petroleum Science and Engineering August 2022 215 Part A
Publikováno v:
European Journal of Ophthalmology; May2024, Vol. 34 Issue 3, pNP42-NP45, 4p
Autor:
Luca Pasquini, Antonio Napolitano, Martina Lucignani, Emanuela Tagliente, Francesco Dellepiane, Maria Camilla Rossi-Espagnet, Matteo Ritrovato, Antonello Vidiri, Veronica Villani, Giulio Ranazzi, Antonella Stoppacciaro, Andrea Romano, Alberto Di Napoli, Alessandro Bozzao
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
Radiomic models outperform clinical data for outcome prediction in high-grade gliomas (HGG). However, lack of parameter standardization limits clinical applications. Many machine learning (ML) radiomic models employ single classifiers rather than ens
Externí odkaz:
https://doaj.org/article/41f565c7ad4b436b98ef650d7c372b72
Publikováno v:
In Journal of Petroleum Science and Engineering August 2019 179:244-256
Akademický článek
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Autor:
Ranazzi, Paulo Henrique
In reservoir engineering, history matching is the technique that reviews the uncertain parameters of a reservoir simulation model in order to obtain a response according to the observed production data. Reservoir properties have uncertainties due to