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Elenco Pubblicazioni - Publications (1433)

F. Di Rienzo, F. Righetti, M. Laurino, A. Greco, C. Marinai, I. Di Mambro, E. Melissa, N. Carbonaro, F. Bossi, G. Rho, L. Arcarisi, M. Zanoletti, P. Bufano, A. Tognetti, C. Vallati, Using Multiple Devices for Patient Monitoring in Clinical Studies: T

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F. Di Rienzo, F. Righetti, M. Laurino, A. Greco, C. Marinai, I. Di Mambro, E. Melissa, N. Carbonaro, F. Bossi, G. Rho, L. Arcarisi, M. Zanoletti, P. Bufano, A. Tognetti, C. Vallati, Using Multiple Devices for…
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C. Marinai, L. Arcarisi, F. Bossi, P. Bufano, F. Di Rienzo, E. Melissa, G. Rho, M. Zanoletti, A. Greco, M. Laurino, C. Vallati, N. Carbonaro, A. Tognetti, Smart Mattress Cover for Unobtrusive Monitoring of SleepQuality Correlates in Real-Life, 2024 I

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C. Marinai, L. Arcarisi, F. Bossi, P. Bufano, F. Di Rienzo, E. Melissa, G. Rho, M. Zanoletti, A. Greco, M. Laurino, C. Vallati, N. Carbonaro, A. Tognetti, Smart Mattress Cover for Unobtrusive Monitoring of SleepQuality…
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Z. Michele, B. Pasquale, B. Francesco, D.R. Francesco, M. Carlotta, R. Gianluca, V. Carlo, C. Nicola, G. Alberto, L. Marco, Combining different wearable devices to assess gait speed in real-world setting, Sensors (2024) 1–22.

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Z. Michele, B. Pasquale, B. Francesco, D.R. Francesco, M. Carlotta, R. Gianluca, V. Carlo, C. Nicola, G. Alberto, L. Marco, Combining different wearable devices to assess gait speed in real-world setting, Sensors(2024) 1–22. https://doi.org/10.3390/s24103205
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F. Bargagna, L. A. De Santi et al.: "Bayesian XAI Methods Towards a Robustness-Centric Approach to Deep Learning: An ABIDE I Study," 2024 IEEE International Symposium on Medical Measurements and Applications, Online

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Abstract: Developing reliable and explainable models is a crucial point to effectively integrate and exploit the potentials offered by Deep Learning (DL) architectures in high-stakes scenarios like healthcare. There are several applications that exploit DL to…
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F. Bargagna, D. Zigrino et al.: "Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images", Journal of Imaging Informatics in Medicine, Online

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Abstract: Medical image classifcation using convolutional neural networks (CNNs) is promising but often requires extensive manual tuning for optimal model defnition. Neural architecture search (NAS) automates this process, reducing human intervention signifcantly. This study applies NAS…
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