Andrea Dosi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Andrea Dosi has a total of 31 co-authors. Here are the top co-authors by number of shared publications:
- Giuseppe Longo
Department of Physics University of Napoli "Federico II" via Cinthia 9 80126 Napoli Italy
2 shared publications - Massimo Brescia
Department of Physics “E. Pancini”, University Federico II, Via Cinthia 6, 80126 Napoli, Italy
2 shared publications - Adriano Ettari
Department of Physics E. Pancini, University of Naples Federico II, Naples, Italy
1 shared publication - Alvi Rownok
Department of Physics E. Pancini, University of Naples Federico II, Naples, Italy
1 shared publication - Anna Di Nardo
University of Naples Federico II, Department of Physics E. Pancini and University of Naples Federico II
1 shared publication - Carlo Donadio
Dipartimento Di Scienze Della Terra Dell’Ambiente E Delle Risorse, Università Degli Studi Di Napoli Federico II, Complesso Universitario Di Monte Sant’Angelo, Naples, Italy
1 shared publication
Latest Publications
- Open Access
- 17 April 2026
- Published by Elsevier in Expert Systems with Applications
- Vol. 324
Less is more: AMBER-AFNO - a new benchmark for lightweight 3D medical image segmentation
- 17 April 2026
- Published by Elsevier in Expert Systems with Applications
- Vol. 324
Open AccessLess is more: AMBER-AFNO - a new benchmark for lightweight 3D medical image segmentation
- 30 May 2025
- Published by Springer Nature in Neural Computing and Applications
- Vol. 37 (22) , 17273-17291
AMBER: advanced SegFormer for multi-band image segmentation—an application to hyperspectral imaging
- 30 May 2025
- Published by Springer Nature in Neural Computing and Applications
- Vol. 37 (22) , 17273-17291
AMBER: advanced SegFormer for multi-band image segmentation—an application to hyperspectral imaging
- Open Access
- 13 December 2023
- Published by MDPI AG in Physical Sciences Forum
- Vol. 9 (1) , 18
A BRAIN Study to Tackle Image Analysis with Artificial Intelligence in the ALMA 2030 Era
An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis, employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at the intersection of observational astronomy, statistics, algorithm development, and data science. In this study, we provide evidence of the benefits of employing these approaches to ALMA imaging for operational and scientific purposes. We show the potential of two techniques, RESOLVE and DeepFocus, applied to ALMA-calibrated science data. Significant advantages are provided with the prospect to improve the quality and completeness of the data products stored in the science archive and the overall processing time for operations. Both approaches evidence the logical pathway to address the incoming revolution in data rates dictated by the planned electronic upgrades. Moreover, we bring to the community additional products through a new package, ALMASim, to promote advancements in these fields, providing a refined ALMA simulator usable by a large community for training and testing new algorithms.
- 13 December 2023
- Published by MDPI AG in Physical Sciences Forum
- Vol. 9 (1) , 18
Open AccessA BRAIN Study to Tackle Image Analysis with Artificial Intelligence in the ALMA 2030 Era
An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis, employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at the intersection of observational astronomy, statistics, algorithm development, and data science. In this study, we provide evidence of the benefits of employing these approaches to ALMA imaging for operational and scientific purposes. We show the potential of two techniques, RESOLVE and DeepFocus, applied to ALMA-calibrated science data. Significant advantages are provided with the prospect to improve the quality and completeness of the data products stored in the science archive and the overall processing time for operations. Both approaches evidence the logical pathway to address the incoming revolution in data rates dictated by the planned electronic upgrades. Moreover, we bring to the community additional products through a new package, ALMASim, to promote advancements in these fields, providing a refined ALMA simulator usable by a large community for training and testing new algorithms.