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