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pro vyhledávání: '"Cabitza P"'
Autor:
Longo, Luca, Brcic, Mario, Cabitza, Federico, Choi, Jaesik, Confalonieri, Roberto, Del Ser, Javier, Guidotti, Riccardo, Hayashi, Yoichi, Herrera, Francisco, Holzinger, Andreas, Jiang, Richard, Khosravi, Hassan, Lecue, Freddy, Malgieri, Gianclaudio, Páez, Andrés, Samek, Wojciech, Schneider, Johannes, Speith, Timo, Stumpf, Simone
Publikováno v:
Information Fusion 2024
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with prac
Externí odkaz:
http://arxiv.org/abs/2310.19775
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss S4, Pp 1-17 (2024)
Abstract Background The frequency of hip and knee arthroplasty surgeries has been rising steadily in recent decades. This trend is attributed to an aging population, leading to increased demands on healthcare systems. Fast Track (FT) surgical protoco
Externí odkaz:
https://doaj.org/article/1bf9163c7af544969858d2535f3ff985
The shift from symbolic AI systems to black-box, sub-symbolic, and statistical ones has motivated a rapid increase in the interest toward explainable AI (XAI), i.e. approaches to make black-box AI systems explainable to human decision makers with the
Externí odkaz:
http://arxiv.org/abs/2210.15236
In medical settings, Individual Variation (IV) refers to variation that is due not to population differences or errors, but rather to within-subject variation, that is the intrinsic and characteristic patterns of variation pertaining to a given insta
Externí odkaz:
http://arxiv.org/abs/2210.04555
This article discusses open problems, implemented solutions, and future research in the area of responsible AI in healthcare. In particular, we illustrate two main research themes related to the work of two laboratories within the Department of Infor
Externí odkaz:
http://arxiv.org/abs/2203.03616
Autor:
Eleonora Cabitza, Marta Pirola, Cinzia Baldessari, Giuditta Bernardelli, Elena Zunarelli, Stefania Pipitone, Maria Giuseppa Vitale, Cecilia Nasso, Eleonora Molinaro, Marco Oltrecolli, Elisa D’Agostino, Vincenzo Dario Mandato, Andrea Palicelli, Massimo Dominici, Roberto Sabbatini
Publikováno v:
Journal of Medical Case Reports, Vol 17, Iss 1, Pp 1-5 (2023)
Abstract Background Ovarian cancer is metastatic at presentation in about 62% of cases, but brain metastases are rare, reported in 3.3–4% of patients. Brain metastasis seems to be more frequent in advanced stages at diagnosis and in patients with B
Externí odkaz:
https://doaj.org/article/a4f5a6c85c3d401a928fbbcb6aa4d4bf
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, 37(6), 6860-6868 (2023)
Most Artificial Intelligence applications are based on supervised machine learning (ML), which ultimately grounds on manually annotated data. The annotation process is often performed in terms of a majority vote and this has been proved to be often p
Externí odkaz:
http://arxiv.org/abs/2109.04270
Autor:
A. Baroncini, A. Campagner, F. Langella, F. Cabitza, F. Barile, R. Cecchinato, M. Damilano, A. Redaelli, D. Vanni, D. Compagnone, P. Berjano
Publikováno v:
Brain and Spine, Vol 4, Iss , Pp 102968- (2024)
Externí odkaz:
https://doaj.org/article/b52c01dd37d14de6b797e17342884d96
Publikováno v:
Frontiers in Surgery, Vol 10 (2023)
Externí odkaz:
https://doaj.org/article/048c3e3cd4ef48058a7c63d2d6d1e9d7
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