Zobrazeno 1 - 10
of 7 024
pro vyhledávání: '"Colantonio A."'
Autor:
Carloni, Gianluca, Colantonio, Sara
The aim of this paper is threefold. We inform the AI practitioner about the human visual system with an extensive literature review; we propose a novel biologically motivated neural network for image classification; and, finally, we present a new plu
Externí odkaz:
http://arxiv.org/abs/2409.04360
Due to domain shift, deep learning image classifiers perform poorly when applied to a domain different from the training one. For instance, a classifier trained on chest X-ray (CXR) images from one hospital may not generalize to images from another h
Externí odkaz:
http://arxiv.org/abs/2408.04949
The graph coloring problem is an optimization problem involving the assignment of one of q colors to each vertex of a graph such that no two adjacent vertices share the same color. This problem is NP-hard and arises in various practical applications.
Externí odkaz:
http://arxiv.org/abs/2408.01503
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 ca
Externí odkaz:
http://arxiv.org/abs/2403.17530
Human pose estimation, the process of identifying joint positions in a person's body from images or videos, represents a widely utilized technology across diverse fields, including healthcare. One such healthcare application involves in-bed pose esti
Externí odkaz:
http://arxiv.org/abs/2402.00700
Autor:
Pachetti, Eva, Colantonio, Sara
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 me
Externí odkaz:
http://arxiv.org/abs/2309.11433
Autor:
Carloni, Gianluca, Colantonio, Sara
We present a novel technique to discover and exploit weak causal signals directly from images via neural networks for classification purposes. This way, we model how the presence of a feature in one part of the image affects the appearance of another
Externí odkaz:
http://arxiv.org/abs/2309.10399
In this paper, we present a novel method to automatically classify 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 that extr
Externí odkaz:
http://arxiv.org/abs/2309.10725
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
Externí odkaz:
http://arxiv.org/abs/2309.09901
Autor:
Conti, Francesco, Banchelli, Martina, Bessi, Valentina, Cecchi, Cristina, Chiti, Fabrizio, Colantonio, Sara, D'Andrea, Cristiano, de Angelis, Marella, Moroni, Davide, Nacmias, Benedetta, Pascali, Maria Antonietta, Sorbi, Sandro, Matteini, Paolo
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS). We investigated whether the raw and preproce
Externí odkaz:
http://arxiv.org/abs/2309.03664