Jose Maria Salinas Serrano

• University of Alicante

Objective To develop an artificial intelligence (AI) system capable of classifying and segmenting femoral fractures. To compare its performance against existing state-of-the-art methods. Methods This Institutional Review Board (IRB)-approved retrospective study did not require informed consent. 10,308 hip x-rays from 2618 patients were retrieved f...

Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) extends RRG by including the localisation of individual findings on the image. Currently, there are no manually annotated chest X-ray (CXR) datasets to train GRRG models. In this work, we present a dataset...

Background Medical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal information from both patients and medical staff. Although currently there are several anonymization strategies for the English language,...

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 detection of few pathologies, we use a hierarchical taxonomy mapped to the Unified Medical Language System (UMLS) terminology to identify 189 radiological findings, 22 differe...

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 reactio...

Medical texts such as radiology reports or electronic health records are a powerful source of data for researchers. Anonymization methods must be developed to de-identify documents containing personal information from both patients and medical staff. Although currently there are several anonymization strategies for the English language, they are al...

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 more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering s...

The management and knowledge extraction system for bioimaging in the cloud (R & D Cloud CEIB) which is proposed in this article will use the services offered by the centralization of bioimaging through Valencian Biobank Medical Imaging (GIMC) as a basis for managing and extracting knowledge from a bioimaging bank, providing that knowledge in the fo...

The management system and knowledge extraction of bioimaging in the cloud (R & D Cloud CEIB) which is proposed in this article will use the services offered by the centralization of bioimaging through Valencian Biobank Medical Imaging (GIMC in Spanish) as a basis for managing and extracting knowledge from a bioimaging bank, providing that knowledge...

The management system and knowledge extraction of bioimaging in the cloud (R & D Cloud CEIB) which is proposed in this article will use the services offered by the centralization of bioimaging through Valencian Biobank Medical Imaging (GIMC in Spanish) as a basis for managing and extracting knowledge from a bioimaging bank, providing that knowledge...

Abstract— The management system and knowledge extraction of bioimaging in the cloud (R & D Cloud CEIB) which is proposed in this article will use the services offered by the centralization of bioimaging through Valencian Biobank Medical Imaging (GIMC in Spanish) as a basis for managing and extracting knowledge from a bioimaging bank, providing that...