L. A. De Santi, F. Bargagna, M. F. Santarelli, V. Positano: “Evaluating explanations of an Alzheimer's Disease 18F-FDG Brain PET black-box classifier”, xAI 2023. Communications in Computer and Information Science, vol 1901. Springer (2023)

eXplainable Artificial Intelligence (XAI) has been increasingly applied to interpret Deep Neural Networks (DNN) in medical imaging applications, but a general consensus about the best interpretation strategy is missing. This is also due to the absence...

PhD Forum Abstract: Efficient Computing and Communication Paradigms for Federated Learning Data Streams

Artificial intelligence of things at the edge: Scalable and efficient distributed learning for massive scenarios

Federated Learning with Time-based Client Selection to Optimize Edge Resources

Drivers Stress Identification in Real-World Driving Tasks

Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks

A Novel Approach to Distributed Model Aggregation using Apache Kafka

AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving

E. Sebastiani, P. Angeletti, G. Bacci, R. de Gaudenzi, M. Luise, and L. Sanguinetti, “Mega Antenna Formation of Satellite Arrays,” in Proc. Intl. Commun. Satellite Systems Conf. (ICSSC), Bradford, UK, Oct. 2023.

Recently, distributed antenna technology based on formation of arrays (FoA) has been proposed in the framework of non-terrestrial network (NTN) integration to provide 5G-like mobile satellite services. To obtain the desired benefits in terms of bitrate...

Kumar, V., Cicconetti, C., Conti, M., & Passarella, A. (2023). Routing in Quantum Repeater Networks with Mixed Noise Figures. arXiv preprint arXiv:2310.08990

Abstract—Quantum network holds the key to the next generation of secure communication, long-distance communication, and quantum internet. Due to inherent quantum effects, routing in the quantum network is a major challenge. This study explores a...

Berti, A., Buongiorno, R., Carloni, G., ... & Colantonio, S. (2023, May). Exploring the potential and challenges of AI in clinical diagnostics and remote assistance of individuals. In Conv Naz CINI sull'Intelligenza Artificiale (ITAL-IA)

Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible,...

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).

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 a causality-extractor module...

Carloni, G., Berti, A., & Colantonio, S. (2023). The role of causality in explainable artificial intelligence. arXiv preprint arXiv:2309.09901.

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 review...

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.

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 risk...

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.

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 risk...

Pachetti, E., & Colantonio, S. (2023). A Systematic Review of Few-Shot Learning in Medical Imaging. arXiv preprint arXiv:2309.11433.

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...

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).

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 a causality-extractor module...

Carloni, G., & Colantonio, S. (2023). Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results. arXiv preprint arXiv:2309.10399.

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 of another feature in a different part of the image....

VIBES: Vibro-Inertial Bionic Enhancement System in a Prosthetic Socket

Authors: Alessia Silvia Ivani, Federica Barontini, Manuel G. Catalano, Giorgio Grioli, Matteo Bianchi and Antonio Bicchi Published in ICORR 2023 Proceedings

M. Moglioni, A. C. Kraan, A. Berti, et al.: “MLS analysis of INSIDE in-beam PET images for the de-tection of morphological changes in patients treated with proton therapy”. In: Nuclear Instruments and Methods in Physics Research

Nome Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment Data pubblicazione: June 2023 Autori: Martina Moglioni, A.C. Kraan, A. Berti, P. Carra, P. Cerello, M. Ciocca,...