144 citations · 178 across the 5 of their papers we have counts for
5 papers
Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data
Michele Svanera, Mattia Savardi, Alberto Signoroni +2
Many clinical and research studies of the human brain require an accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acqu…
BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset
Alberto Signoroni, Mattia Savardi, Sergio Benini +8
In this work we design 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 COVI…
CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI
Dennis Bontempi, Sergio Benini, Alberto Signoroni +2
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Cur…
Who is the director of this movie? Automatic style recognition based on shot features
Michele Svanera, Mattia Savardi, Alberto Signoroni +2
We show how low-level formal features, such as shot duration, meant as length of camera takes, and shot scale, i.e. the distance between the camera and the subject, are distinctive…
Deep driven fMRI decoding of visual categories
Michele Svanera, Sergio Benini, Gal Raz +3
Deep neural networks have been developed drawing inspiration from the brain visual pathway, implementing an end-to-end approach: from image data to video object classes. However bu…