Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Barbano, Carlo Alberto"'
Autor:
Ciranni, Massimiliano, Molinaro, Luca, Barbano, Carlo Alberto, Fiandrotti, Attilio, Murino, Vittorio, Pastore, Vito Paolo, Tartaglione, Enzo
In the last few years, due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities, increasingly more concerns about potential biases to specific, non-representative patterns have been raised. Many works f
Externí odkaz:
http://arxiv.org/abs/2408.09570
Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the most predominant biomarkers in neuroimaging is represented by brain age, which has been shown to be
Externí odkaz:
http://arxiv.org/abs/2408.07079
Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, which may not adequately account for the diverse staining patterns of datasets collected in practical scenarios. In
Externí odkaz:
http://arxiv.org/abs/2406.02077
Autor:
Patrício, Cristiano, Barbano, Carlo Alberto, Fiandrotti, Attilio, Renzulli, Riccardo, Grangetto, Marco, Teixeira, Luis F., Neves, João C.
Contrastive Analysis (CA) regards the problem of identifying patterns in images that allow distinguishing between a background (BG) dataset (i.e. healthy subjects) and a target (TG) dataset (i.e. unhealthy subjects). Recent works on this topic rely o
Externí odkaz:
http://arxiv.org/abs/2406.00772
Autor:
Barbano, Carlo Alberto, Renzulli, Riccardo, Grosso, Marco, Basile, Domenico, Busso, Marco, Grangetto, Marco
In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from
Externí odkaz:
http://arxiv.org/abs/2405.11598
Autor:
Gallone, Guglielmo, Iodice, Francesco, Presta, Alberto, Tore, Davide, de Filippo, Ovidio, Visciano, Michele, Barbano, Carlo Alberto, Serafini, Alessandro, Gorrini, Paola, Bruno, Alessandro, Marra, Walter Grosso, Hughes, James, Iannaccone, Mario, Fonio, Paolo, Fiandrotti, Attilio, Depaoli, Alessandro, Grangetto, Marco, de Ferrari, Gaetano Maria, D'Ascenzo, Fabrizio
Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was develope
Externí odkaz:
http://arxiv.org/abs/2403.18756
Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and find new biomarkers. To estimate accurate and generalizable models, larg
Externí odkaz:
http://arxiv.org/abs/2211.08326
Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased d
Externí odkaz:
http://arxiv.org/abs/2211.05568
Data augmentation is a crucial component in unsupervised contrastive learning (CL). It determines how positive samples are defined and, ultimately, the quality of the learned representation. In this work, we open the door to new perspectives for CL b
Externí odkaz:
http://arxiv.org/abs/2206.01646
Deep neural networks are known for their inability to learn robust representations when biases exist in the dataset. This results in a poor generalization to unbiased datasets, as the predictions strongly rely on peripheral and confounding factors, w
Externí odkaz:
http://arxiv.org/abs/2204.12941