Elenco Pubblicazioni - Publications (1524)
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.…
Pachetti, E. & Colantonio, S. (2024). Seeing More with Less: Meta-Learning and Diffusion Models for Tumor Characterization in Low-data Settings
Written by EVA PACHETTI
While deep learning excels in many areas, its application in medicine is hindered by limited data, which restricts model generalizability. Few-shot learning has emerged as a potential solution to this problem. In this work, we…
Pachetti, E., Tsaftaris, S. A., & Colantonio, S. (2024). Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification. arXiv preprint arXiv:2403.17530.
Written by EVA PACHETTI
Background and objective: Employing deep learning models in critical domains such as medical imaging poses challenges associated with the limited availability of training data. We present a strategy for improving the performance and generalization capabilities…
Pachetti, E., Del Corso, G., Bardelli, S., & Colantonio, S. (2024). Few-Shot Conditional Learning: Automatic and Reliable Device Classification for Medical Test Equipment. Journal of Imaging, 10(7).
Written by EVA PACHETTI
The limited availability of specialized image databases (particularly in hospitals, where tools vary between providers) makes it difficult to train deep learning models. This paper presents a few-shot learning methodology that uses a pre-trained ResNet…
