Zobrazeno 1 - 10
of 538
pro vyhledávání: '"Castelluccia, A."'
Machine learning algorithms are increasingly used to assist human decision-making. When the goal of machine assistance is to improve the accuracy of human decisions, it might seem appealing to design ML algorithms that complement human knowledge. Whi
Externí odkaz:
http://arxiv.org/abs/2209.03821
Autor:
Ienca, Marcello, Fins, Joseph J., Jox, Ralf J., Jotterand, Fabrice, Voeneky, Silja, Andorno, Roberto, Ball, Tonio, Castelluccia, Claude, Chavarriaga, Ricardo, Chneiweiss, Hervé, Ferretti, Agata, Friedrich, Orsolya, Hurst, Samia, Merkel, Grischa, Molnar-Gabor, Fruzsina, Rickli, Jean-Marc, Scheibner, James, Vayena, Effy, Yuste, Rafael, Kellmeyer, Philipp
The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and governance. We identify distinctive ethical impl
Externí odkaz:
http://arxiv.org/abs/2109.11960
Autor:
Castelluccia, Alessandra, Tramacere, Francesco, Colciago, Riccardo Ray, Borgia, Marzia, Sallustio, Alessandra, Proto, Tiziana, Portaluri, Maurizio, Arcangeli, Prof Stefano
Publikováno v:
In Clinical Genitourinary Cancer August 2024 22(4)
Autor:
Gamze Ugurluer, Famke L. Schneiders, Stefanie Corradini, Luca Boldrini, Rupesh Kotecha, Patrick Kelly, Lorraine Portelance, Philip Camilleri, Merav A. Ben-David, Spencer Poiset, Sebastian N. Marschner, Giulia Panza, Tugce Kutuk, Miguel A. Palacios, Alessandra Castelluccia, Teuta Zoto Mustafayev, Banu Atalar, Suresh Senan, Enis Ozyar
Publikováno v:
Clinical and Translational Radiation Oncology, Vol 46, Iss , Pp 100756- (2024)
Purpose: Stereotactic body radiotherapy (SBRT) is an effective treatment for adrenal gland metastases, but it is technically challenging and there are concerns about toxicity. We performed a multi-institutional pooled retrospective analysis to study
Externí odkaz:
https://doaj.org/article/64fa49a7a6fb4ff48daf6a78a0b62c7e
Autor:
Alessandra Castelluccia, Angela Sardaro, Artor Niccoli Asabella, Antonio Rosario Pisani, Dino Rubini, Maurizio Portaluri, Francesco Tramacere
Publikováno v:
Clinical Case Reports, Vol 12, Iss 4, Pp n/a-n/a (2024)
Key Clinical Message PET‐driven SBRT plus pembrolizumab as first‐line therapy against pleomorphic Pancoast cancer appears beneficial, probably due to high equivalent doses of SBRT on photopenic necrotic core and synergic immune system stimulation
Externí odkaz:
https://doaj.org/article/587802d17c704d05ba424b244946967d
Autor:
Ugurluer, Gamze, Schneiders, Famke L., Corradini, Stefanie, Boldrini, Luca, Kotecha, Rupesh, Kelly, Patrick, Portelance, Lorraine, Camilleri, Philip, Ben-David, Merav A., Poiset, Spencer, Marschner, Sebastian N., Panza, Giulia, Kutuk, Tugce, Palacios, Miguel A., Castelluccia, Alessandra, Zoto Mustafayev, Teuta, Atalar, Banu, Senan, Suresh, Ozyar, Enis
Publikováno v:
In Clinical and Translational Radiation Oncology May 2024 46
Federated learning becomes a prominent approach when different entities want to learn collaboratively a common model without sharing their training data. However, Federated learning has two main drawbacks. First, it is quite bandwidth inefficient as
Externí odkaz:
http://arxiv.org/abs/2103.00342
Autor:
Castelluccia, Alessandra1 (AUTHOR) alessandra.castelluccia@asl.brindisi.it, Sardaro, Angela2 (AUTHOR), Niccoli Asabella, Artor3 (AUTHOR), Pisani, Antonio Rosario3 (AUTHOR), Rubini, Dino4 (AUTHOR), Portaluri, Maurizio1 (AUTHOR), Tramacere, Francesco1 (AUTHOR)
Publikováno v:
Clinical Case Reports. Apr2024, Vol. 12 Issue 4, p1-5. 5p.
Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by exchanging only parameter updates, it remains vulnerable to various inferen
Externí odkaz:
http://arxiv.org/abs/2011.05578
Federated Learning enables entities to collaboratively learn a shared prediction model while keeping their training data locally. It prevents data collection and aggregation and, therefore, mitigates the associated privacy risks. However, it still re
Externí odkaz:
http://arxiv.org/abs/2010.07808