M
Mattia Savardi
Researcher at University of Brescia
Publications - 27
Citations - 417
Mattia Savardi is an academic researcher from University of Brescia. The author has contributed to research in topics: Convolutional neural network & Deep learning. The author has an hindex of 7, co-authored 21 publications receiving 213 citations.
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Journal ArticleDOI
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.
TL;DR: The present review is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields and the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectrals data from a multidisciplinary perspective.
Journal ArticleDOI
BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset.
Alberto Signoroni,Mattia Savardi,Sergio Benini,Nicola Adami,Riccardo Leonardi,Paolo Gibellini,Filippo Vaccher,Marco Ravanelli,Andrea Borghesi,Roberto Maroldi,Davide Farina +10 more
TL;DR: In this paper, an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients was proposed.
Posted Content
End-to-end learning for semiquantitative rating of COVID-19 severity on Chest X-rays
Alberto Signoroni,Mattia Savardi,Sergio Benini,Nicola Adami,Riccardo Leonardi,Paolo Gibellini,Filippo Vaccher,Marco Ravanelli,Andrea Borghesi,Roberto Maroldi,Davide Farina +10 more
TL;DR: This work designs an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients and shows that the solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring.
Proceedings ArticleDOI
Shot Scale Analysis in Movies by Convolutional Neural Networks
TL;DR: This work proposes to use Convolutional Neural Networks for the automatic classification of shot scale into Close-, Medium-, or Long-shots, widely superior to state-of-the-art, with an overall accuracy around 94%.
Journal ArticleDOI
Automatic hemolysis identification on aligned dual-lighting images of cultured blood agar plates.
TL;DR: The suitability of the design and evaluation of a method for robust hemolysis detection and classification, which remains feasible even in challenging conditions (low contrast or illumination changes), are demonstrated.