107 citations · 117 across the 4 of their papers we have counts for
5 papers
CAHAL: Clinically Applicable resolution enHAncement for Low-resolution MRI scans
Sergio Morell-Ortega, Ángela González-Cebrián, Boris Mansencal +8
Large-scale automated morphometric analysis of brain MRI is limited by the thick-slice, anisotropic acquisitions prevalent in routine clinical practice. Existing generative super-r…
UMLS-ChestNet: A deep convolutional neural network for radiological findings, differential diagnoses and localizations of COVID-19 in chest x-rays
Germán González, Aurelia Bustos, José María Salinas +7
In this work we present a method for the detection of radiological findings, their location and differential diagnoses from chest x-rays. Unlike prior works that focus on the detec…
BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
Maria de la Iglesia Vayá, Jose Manuel Saborit, Joaquim Angel Montell +10
This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) im…
Deep ICE: A Deep learning approach for MRI Intracranial Cavity Extraction
José V. Manjón, Jose E. Romero, Roberto Vivo-Hernando +4
Automatic methods for measuring normalized regional brain volumes from MRI data are a key tool to help in the objective diagnostic and follow-up of many neurological diseases. To e…
PadChest: A large chest x-ray image dataset with multi-label annotated reports
Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas +1
We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes mor…