Zobrazeno 1 - 10
of 371
pro vyhledávání: '"Salinas, Josè"'
Autor:
Castro, Daniel C., Bustos, Aurelia, Bannur, Shruthi, Hyland, Stephanie L., Bouzid, Kenza, Wetscherek, Maria Teodora, Sánchez-Valverde, Maria Dolores, Jaques-Pérez, Lara, Pérez-Rodríguez, Lourdes, Takeda, Kenji, Salinas, José María, Alvarez-Valle, Javier, Herrero, Joaquín Galant, Pertusa, Antonio
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 man
Externí odkaz:
http://arxiv.org/abs/2411.05085
Autor:
Pappada, Scott M., Owais, Mohammad Hamza, Feeney, John J., Salinas, Jose, Chaney, Benjamin, Duggan, Joan, Sparkle, Tanaya, Aouthmany, Shaza, Hinch, Bryan, Papadimos, Thomas J. ⁎
Publikováno v:
In Anaesthesia Critical Care & Pain Medicine December 2024 43(6)
Autor:
ArabiDarrehDor, Ghazal, Tivay, Ali, Meador, Chris, Kramer, George C., Hahn, Jin-Oh, Salinas, Jose
Existing burn resuscitation protocols exhibit large variability in treatment efficacy. Hence, they must be further optimized based on comprehensive knowledge of burn pathophysiology. A physics-based mathematical model that can replicate physiological
Externí odkaz:
http://arxiv.org/abs/2110.13909
Autor:
ArabiDarrehDor, Ghazal, Tivay, Ali, Bighamian, Ramin, Meador, Chris, Kramer, George C., Hahn, Jin-Oh, Salinas, Jose
Publikováno v:
Burns, 47(2), pp.371-386 (2021)
This paper presents a mathematical model of blood volume kinetics and renal function in response to burn injury and resuscitation, which is applicable to the development and non-clinical testing of burn resuscitation protocols and algorithms. Prior m
Externí odkaz:
http://arxiv.org/abs/2110.11933
Publikováno v:
In Burns August 2024 50(6):1513-1518
Introduction: Real-world data generated from clinical practice can be used to analyze the real-world evidence (RWE) of COVID-19 pharmacotherapy and validate the results of randomized clinical trials (RCTs). Machine learning (ML) methods are being use
Externí odkaz:
http://arxiv.org/abs/2107.10239
Autor:
Sánchez-García, Rubén E., Castilleja-Escobedo, Orlando, Salmón-Folgueras, Rodrigo, López-Salinas, José Luis
Publikováno v:
In Current Research in Food Science 2024 9
Autor:
González, Germán, Bustos, Aurelia, Salinas, José María, de la Iglesia-Vaya, María, Galant, Joaquín, Cano-Espinosa, Carlos, Barber, Xavier, Orozco-Beltrán, Domingo, Cazorla, Miguel, Pertusa, Antonio
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
Externí odkaz:
http://arxiv.org/abs/2006.05274
Autor:
Vayá, Maria de la Iglesia, Saborit, Jose Manuel, Montell, Joaquim Angel, Pertusa, Antonio, Bustos, Aurelia, Cazorla, Miguel, Galant, Joaquin, Barber, Xavier, Orozco-Beltrán, Domingo, García-García, Francisco, Caparrós, Marisa, González, Germán, Salinas, Jose María
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
Externí odkaz:
http://arxiv.org/abs/2006.01174
Publikováno v:
Med. Image Anal., 66 (2020), 101797
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 interpre
Externí odkaz:
http://arxiv.org/abs/1901.07441