no code implementations • 28 Oct 2021 • Aurelia Bustos, Artemio Payá, Andres Torrubia, Rodrigo Jover, Xavier Llor, Xavier Bessa, Antoni Castells, Cristina Alenda
A systematic study of biases at tile level identified three protected (bias) variables associated with the learned representations of a baseline model: the project of origin of samples, the patient spot and the TMA glass where each spot was placed.
no code implementations • 19 Jul 2021 • Aurelia Bustos, Patricio Mas_Serrano, Mari L. Boquera, Jose M. Salinas
2390 admissions from 2 additional health departments were reserved as an independent test to analyze retrospectively the survival benefits of therapies in the population selected by the TE-ML models using cox-proportional hazard models.
no code implementations • 6 Jun 2020 • Germán González, Aurelia Bustos, José María Salinas, María de la Iglesia-Vaya, Joaquín Galant, Carlos Cano-Espinosa, Xavier Barber, Domingo Orozco-Beltrán, Miguel Cazorla, Antonio Pertusa
In this work we present a method for the detection of radiological findings, their location and differential diagnoses from chest x-rays.
2 code implementations • 1 Jun 2020 • Maria de la Iglesia Vayá, Jose Manuel Saborit, Joaquim Angel Montell, Antonio Pertusa, Aurelia Bustos, Miguel Cazorla, Joaquin Galant, Xavier Barber, Domingo Orozco-Beltrán, Francisco García-García, Marisa Caparrós, Germán González, Jose María Salinas
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) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests.
4 code implementations • 22 Jan 2019 • Aurelia Bustos, Antonio Pertusa, Jose-Maria Salinas, Maria de la Iglesia-Vayá
We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports.
1 code implementation • 22 Mar 2018 • Aurelia Bustos, Antonio Pertusa
A text classifier was trained using deep neural networks, with pre-trained word-embeddings as inputs, to predict whether or not short free-text statements describing clinical information were considered eligible.