Francisco Javier Pérez-Benito

According to our database, Francisco Javier Pérez-Benito authored at least 8 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Three-Blind Validation Strategy of Deep Learning Models for Image Segmentation.

Andrés Larroza, Francisco Javier Pérez-Benito, Raquel Tendero, Juan-Carlos Perez-Cortes, Marta Román, Rafael Llobet

J. Imaging, 2025

2022

A deep learning framework to classify breast density with noisy labels regularization.

Hector Lopez-Almazan, Francisco Javier Pérez-Benito, Andrés Larroza, Juan-Carlos Perez-Cortes, Marina Pollán, Beatriz Pérez-Gómez, Dolores Salas-Trejo, María Casals, Rafael Llobet

Comput. Methods Programs Biomed., 2022

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks.

Vicent Ortiz Castelló, Francisco Javier Pérez-Benito, Omar del Tejo Catalá, Ismael Salvador Igual, Rafael Llobet, Juan-Carlos Perez-Cortes

IEEE Access, 2022

2021

Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients.

Omar del Tejo Catalá, Ismael Salvador Igual, Francisco Javier Pérez-Benito, David Millán Escrivá, Vicent Ortiz Castelló, Rafael Llobet, Juan-Carlos Perez-Cortes

IEEE Access, 2021

2020

Healthcare data heterogeneity and its contribution to machine learning performance.

Francisco Javier Pérez-Benito

PhD thesis, 2020

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation.

Francisco Javier Pérez-Benito, François Signol, Juan-Carlos Perez-Cortes, Alejandro Fuster Baggetro, Marina Pollán, Beatriz Pérez-Gómez, Dolores Salas-Trejo, María Casals, Inmaculada Martínez, Rafael Llobet

Comput. Methods Programs Biomed., 2020

2019

A happiness degree predictor using the conceptual data structure for deep learning architectures.

Francisco Javier Pérez-Benito, Patricia Villacampa-Fernández, J. Alberto Conejero, Juan M. García-Gómez, Esperanza Navarro-Pardo

Comput. Methods Programs Biomed., 2019

Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.

Francisco Javier Pérez-Benito, François Signol, Juan-Carlos Perez-Cortes, Marina Pollán, Beatriz Pérez-Gómez, Dolores Salas-Trejo, María Casals, Inmaculada Martínez, Rafael Llobet

Comput. Methods Programs Biomed., 2019