Elenco Pubblicazioni - Publications (1288)
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. (2023). A Systematic Review of Few-Shot Learning in Medical Imaging. arXiv preprint arXiv:2309.11433.
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…
Carloni, G., & Colantonio, S. (2023). Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results. arXiv preprint arXiv:2309.10399.
Written by GIANLUCA CARLONI
We present a new method for automatically classifying medical images that uses weak causal signals in the scene to model how the presence of a feature in one part of the image affects the appearance…
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