Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Paolo Soda"'
Autor:
Lorenzo Tronchin, Ermanno Cordelli, Lorenzo Ricciardi Celsi, Daniele Maccagnola, Massimo Natale, Paolo Soda, Rosa Sicilia
Publikováno v:
IEEE Access, Vol 12, Pp 27484-27500 (2024)
As Artificial Intelligence (AI) is becoming part of our daily lives, the need to understand and trust its decisions is becoming a pressing issue. EXplainable AI (XAI) aims at answering this demand, providing tools to get insights into the models’ b
Externí odkaz:
https://doaj.org/article/85f104f2c1004b65a0815692ba65591b
Publikováno v:
Frontiers in Robotics and AI, Vol 11 (2024)
Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The poten
Externí odkaz:
https://doaj.org/article/5d8090d554e8459c8abd719b7acd16f1
Autor:
Francesco Prata, Umberto Anceschi, Ermanno Cordelli, Eliodoro Faiella, Angelo Civitella, Piergiorgio Tuzzolo, Andrea Iannuzzi, Alberto Ragusa, Francesco Esperto, Salvatore Mario Prata, Rosa Sicilia, Giovanni Muto, Rosario Francesco Grasso, Roberto Mario Scarpa, Paolo Soda, Giuseppe Simone, Rocco Papalia
Publikováno v:
Current Oncology, Vol 30, Iss 2, Pp 2021-2031 (2023)
Background: The aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer. Methods: From September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy a
Externí odkaz:
https://doaj.org/article/12298fa3abdc462098d04159c627878b
Publikováno v:
IEEE Access, Vol 11, Pp 122399-122410 (2023)
The main objective of firefighters is to optimise readiness in response to hazardous events and to minimise their collateral effects. In this context, few but growing research is investigating machine learning algorithms to support firefighters’ wo
Externí odkaz:
https://doaj.org/article/31a6e29d680e434796264d7f487e4a72
Autor:
Matteo Tortora, Ermanno Cordelli, Rosa Sicilia, Lorenzo Nibid, Edy Ippolito, Giuseppe Perrone, Sara Ramella, Paolo Soda
Publikováno v:
IEEE Access, Vol 11, Pp 47563-47578 (2023)
Current practice in cancer treatment collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. The importance of these data sources taken individually has fostered the recent rise of radiomics and pathomic
Externí odkaz:
https://doaj.org/article/d7b5d00fee2245a7989132a6f32c1173
Autor:
Eliodoro Faiella, Daniele Vertulli, Francesco Esperto, Ermanno Cordelli, Paolo Soda, Rosa Maria Muraca, Lorenzo Paolo Moramarco, Rosario Francesco Grasso, Bruno Beomonte Zobel, Domiziana Santucci
Publikováno v:
Tomography, Vol 8, Iss 4, Pp 2010-2019 (2022)
Background: To evaluate the clinical utility of an Artificial Intelligence (AI) radiology solution, Quantib Prostate, for prostate cancer (PCa) lesions detection on multiparametric Magnetic Resonance Images (mpMRI). Methods: Prostate mpMRI exams of 1
Externí odkaz:
https://doaj.org/article/c31c4dabb0cc41ab81c0b7b37864596d
Publikováno v:
IEEE Access, Vol 10, Pp 112713-112720 (2022)
The exponential growth of IoT devices, smartphones, smartwatches, and vehicles equipped with positioning technology, such as Global Positioning System (GPS) modules, has boosted the development of location-based services for several applications in I
Externí odkaz:
https://doaj.org/article/fd9cb747601949bfb5ae123120f49908
Autor:
Matteo Testi, Matteo Ballabio, Emanuele Frontoni, Giulio Iannello, Sara Moccia, Paolo Soda, Gennaro Vessio
Publikováno v:
IEEE Access, Vol 10, Pp 63606-63618 (2022)
Over the past few decades, the substantial growth in enterprise-data availability and the advancements in Artificial Intelligence (AI) have allowed companies to solve real-world problems using Machine Learning (ML). ML Operations (MLOps) represents a
Externí odkaz:
https://doaj.org/article/a0c995ee780945a080b2c4ef9357978d
Autor:
Lorenzo Nibid, Carlo Greco, Ermanno Cordelli, Giovanna Sabarese, Michele Fiore, Charles Z. Liu, Edy Ippolito, Rosa Sicilia, Marianna Miele, Matteo Tortora, Chiara Taffon, Mehrdad Rakaee, Paolo Soda, Sara Ramella, Giuseppe Perrone
Publikováno v:
PLoS ONE, Vol 18, Iss 11 (2023)
Externí odkaz:
https://doaj.org/article/a159c855732e4e1682f97826fbed86ef
Publikováno v:
BMC Bioinformatics, Vol 22, Iss 1, Pp 1-15 (2021)
Abstract Background Biological phenomena usually evolves over time and recent advances in high-throughput microscopy have made possible to collect multiple 3D images over time, generating $$3D+t$$ 3 D + t (or 4D) datasets. To extract useful informati
Externí odkaz:
https://doaj.org/article/8a75fb8460ff40b2bf2870022517c38c