Elenco Pubblicazioni - Publications (1521)
Carloni, G., Berti, A., & Colantonio, S. (2024). The role of causality in explainable artificial intelligence. arXiv preprint arXiv:2309.09901.
Written by GIANLUCA CARLONI
Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of…
Xue, Y., Du, Y., Carloni, G., Pachetti, E., Jordan, C., & Tsaftaris, S. A. (2023). Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement. arXiv preprint arXiv:2309.13385.
Written by GIANLUCA CARLONI
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner. Undersampling of the k-space is employed to reduce the scan duration, thus increasing patient comfort and reducing the…
Xue, Y., Du, Y., Carloni, G., Pachetti, E., Jordan, C., Tsaftaris, S.A. (2024). Cine Cardiac MRI Reconstruction Using a Convolutional Recurrent Network with Refinement. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart.
Written by EVA PACHETTI
Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's function and condition in a non-invasive manner. Undersampling of the k-space is employed to reduce the scan duration, thus increasing patient comfort and reducing the…
Pachetti, E., & Colantonio, S. (2024). A systematic review of few-shot learning in medical imaging. Artificial Intelligence in Medicine, 102949.
Written by EVA PACHETTI
The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis, especially with meta-learning.…
Carloni, G., Pachetti, E., & Colantonio, S. (2023). Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2616-2624).
Written by EVA PACHETTI
In this paper, we present a novel method for the automatic classification of medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and…
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