Elenco Pubblicazioni - Publications (1445)
Del Corso, G., Pachetti, E., ... & ProCAncer-I Consortium. (2024, May). Radiomics-Based Reliable Predictions of Side Effects After Radiotherapy for Prostate Cancer. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE.
Written by EVA PACHETTI
This work offers insight into the effectiveness of probabilistic models, specifically those based on ensemble approximations, in predicting adverse side effects following radiotherapy for prostate cancer. We trained a random forest model on radiomic features…
Design Criteria of High-Temperature Integrated Circuits Using Standard SOI CMOS Process up to 300°C
Written by CHRISTIAN SBRANA
C. Sbrana, A. Catania, M. Paliy, S. Strangio, M. Macucci, G. Iannaccone, "Design Criteria of High-Temperature Integrated Circuits Using Standard SOI CMOS Process up to 300°C," in IEEE Access, vol. 12, pp. 57236-57249, 2024, doi:…
Federico Giulio, Carrara Fabio, Amato Giuseppe and Di Benedetto Marco: "Spatio-Temporal 3D Reconstruction from Frame Sequences and Feature Points"
Written by GIULIO FEDERICO
Reconstructing a large real environment is a fundamental task to promote eXtended Reality adoption in industrial and entertainment fields. However, the short range of depth cameras, the sparsity of LiDAR sensors, and the huge computational…
Colantonio, S., Berti, A., Buongiorno, R., Del Corso, G., Pachetti, E., Pascali, M. A., ... & Tsiknakis, M. (2023, December). AI trustworthiness in prostate cancer imaging: a look at algorithmic and system transparency. In 2023 IEEE EMBS Special Topi
Written by EVA PACHETTI
A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favorable…
Pachetti, E., & Colantonio, S. (2024). Hallucinating for Diagnosing: One-Shot Medical Image Classification Leveraging Score-Based Generative Models. In Medical Imaging with Deep Learning.
Written by EVA PACHETTI
Deep learning models in data-scarce domains, such as medical imaging, often suffer from poor performance due to the challenges of acquiring large amounts of labeled data. Few-shot learning offers a promising solution to this problem.…
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- Assistant Professor Xiaomeng Li, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, "Deep Learning for Medical Image Analysis", 21-23, 28 July 2025
- Modifica Lezione - Dr. Adrian Wallwork, "Academic English: Writing and Presenting WITH the support of LLMs", 6,7,11,13,18,20,25,27 March 2025
- Prof. Sajal K. Das, Missouri University of Science and Technology Rolla, MO, USA, "Secure and Resilient Cyber-Physical Systems for Smart Living", 12-15 May 2025
- MODIFICA DATE CORSO - Prof. Nicola Tonellotto, University of Pisa – Italy, PhD. Guglielmo Faggioli, Università degli Studi di Padova - Italy, "Privacy Preserving Information Access", 19-23 May 2025
