frapebe@doctor.upv.es
@iti.es
Associate Professor and Researcher
Universitat Politècnica de València / Instituto Tecnológico de la Informática
EDUCATION
Ph.D. Mathematics and Technical Engineering in Computers
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Andrés Larroza, Francisco Javier Pérez-Benito, Juan-Carlos Perez-Cortes, Marta Román, Marina Pollán, Beatriz Pérez-Gómez, Dolores Salas-Trejo, María Casals, and Rafael Llobet
MDPI AG
Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)—YNet model for the segmentation step. This architecture includes networks to model each radiologist’s noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose “for presentation” mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist’s label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.
Vicent Ortiz Castello, Francisco Javier Perez-Benito, Omar Del Tejo Catala, Ismael Salvador Igual, Rafael Llobet, and Juan-Carlos Perez-Cortes
Institute of Electrical and Electronics Engineers (IEEE)
The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm’s accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. However, this is an extremely simple approach that has no predictive power and does not describe other dataset features that could lead to a more demanding baseline. In this paper, we present the Extended A Priori Probability (EAPP), a novel semi-supervised baseline metric for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset as well as other features that could provide a relatively trivial separability of the target classes. The approach is based on the area under the ROC curve (AUC ROC), known to be quite insensitive to class imbalance. The procedure involves multiobjective feature extraction and a clustering stage in the input space with autoencoders and a subsequent combinatory weighted assignation from clusters to classes depending on the distance to nearest clusters for each class. Class labels are then assigned to establish the combination that maximizes AUC ROC for each number of clusters considered. To avoid overfit in the combined feature extraction and clustering method, a cross-validation scheme is performed in each case. EAPP is defined for different numbers of clusters, starting from the inverse of the minority class proportion, which is useful for a fair comparison among diversely imbalanced datasets. A high EAPP usually relates to an easy binary classification task, but it also may be due to a significant coarse-grained bias in the dataset, when the task is previously known to be difficult. This metric represents a baseline beyond the a priori probability to assess the actual capabilities of binary classification models.
Omar Del Tejo Catala, Ismael Salvador Igual, Francisco Javier Perez-Benito, David Millan Escriva, Vicent Ortiz Castello, Rafael Llobet, and Juan-Carlos Perez-Cortes
Institute of Electrical and Electronics Engineers (IEEE)
Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray devices are already available in health centers and images are obtained immediately. Some datasets containing X-ray images with cases (pneumonia or COVID-19) and controls have been made available to develop machine-learning-based methods to aid in diagnosing the disease. However, these datasets are mainly composed of different sources coming from pre-COVID-19 datasets and COVID-19 datasets. Particularly, we have detected a significant bias in some of the released datasets used to train and test diagnostic systems, which might imply that the results published are optimistic and may overestimate the actual predictive capacity of the techniques proposed. In this article, we analyze the existing bias in some commonly used datasets and propose a series of preliminary steps to carry out before the classic machine learning pipeline in order to detect possible biases, to avoid them if possible and to report results that are more representative of the actual predictive power of the methods under analysis.
Francisco Javier Pérez-Benito, François Signol, Juan-Carlos Perez-Cortes, Alejandro Fuster-Baggetto, Marina Pollan, Beatriz Pérez-Gómez, Dolores Salas-Trejo, Maria Casals, Inmaculada Martínez, and Rafael LLobet
Elsevier BV
Francisco Javier Pérez‐Benito, Juan Miguel García‐Gómez, Esperanza Navarro‐Pardo, and J. Alberto Conejero
Wiley
Deep neural networks (DNNs) have emerged as a state‐of‐the‐art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. Some of the main disadvantages of these trending models are that the choice of the network underlying architecture profoundly influences the performance of the model and that the architecture design requires prior knowledge of the field of study. The use of questionnaires is hugely extended in social/behavioral sciences. The main contribution of this work is to automate the process of a DNN architecture design by using an agglomerative hierarchical algorithm that mimics the conceptual structure of such surveys. Although the train had regression purposes, it is easily convertible to deal with classification tasks. Our proposed methodology will be tested with a database containing socio‐demographic data and the responses to five psychometric Likert scales related to the prediction of happiness. These scales have been already used to design a DNN architecture based on the subdimension of the scales. We show that our new network configurations outperform the previous existing DNN architectures.
Francisco J. Pérez‐Benito, J. Alberto Conejero, Carlos Sáez, Juan M. García‐Gómez, Esperanza Navarro‐Pardo, Lidiane L. Florencio, and César Fernández‐de‐las‐Peñas
Wiley
Migraine is a heterogeneous condition with multiple clinical manifestations. Machine learning algorithms permit the identification of population groups, providing analytical advantages over other modeling techniques.
Pedro A. Solares-Hernández, Fernando A. Manzano, Francisco J. Pérez-Benito, and J. Alberto Conejero
MDPI AG
The Pascal triangle is so simple and rich that it has always attracted the interest of professional and amateur mathematicians. Their coefficients satisfy a myriad of properties. Inspired by the work of Shekatkar et al., we study the divisibility patterns within the elements of the Pascal triangle, through its decomposition into Pascal’s matrices, from the perspective of network science. Applying Kolmogorov–Smirnov test, we determine that the degree distribution of the resulting network follows a power-law distribution. We also study degrees, global and local clustering coefficients, stretching graph, averaged path length and the mixing assortative.
Francisco Javier Pérez-Benito, Carlos Sáez, J. Alberto Conejero, Salvador Tortajada, Bernardo Valdivieso, and Juan M. García-Gómez
Public Library of Science (PLoS)
Objective To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. Materials and methods Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index. Results Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. Discussion TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities’ relocation and increment of citizens (findings 1, 3–4), the impact of strategies (findings 2–3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse. Conclusions The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.
Francisco Javier Pérez-Benito, Francois Signol, Juan-Carlos Pérez-Cortés, Marina Pollán, Beatriz Pérez-Gómez, Dolores Salas-Trejo, María Casals, Inmaculada Martínez, and Rafael LLobet
Elsevier BV
Francisco Javier Pérez-Benito, Patricia Villacampa-Fernández, J. Alberto Conejero, Juan M. García-Gómez, and Esperanza Navarro-Pardo
Elsevier BV