Search Results for author:
Found 4 papers, 2 papers with code
Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data
We ensure robustness across sites by training the model on an unprecedented rich dataset aggregating data from open repositories: almost 27, 000 T1w volumes from around 160 acquisition sites, at 1. 5 - 3T, from a population spanning from 8 to 90 years old.
CineScale: A dataset of cinematic shot scale in movies
We provide a database containing shot scale annotations (i. e., the apparent distance of the camera from the subject of a filmed scene) for more than 792, 000 image frames.
BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset
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 COVID-19 patients.
CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI
Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans.
