Persona: Rincón Zamorano, Mariano
Cargando...
Dirección de correo electrónico
mrincon@dia.uned.es
ORCID
0000-0002-0138-4662
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Rincón Zamorano
Nombre de pila
Mariano
Nombre
15 resultados
Resultados de la búsqueda
Mostrando 1 - 10 de 15
Publicación Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions(ELSEVIER, 2025-07-19) Redondo, Alejandro; Ivaylova, Katerina; Bachiller Mayoral, Margarita; Rincón Zamorano, Mariano; Cuadra Troncoso, José Manuel; Tamimi, Faleh; López Cedrún, José Luis; Diniz Freitas, Márcio; Lago Méndez, Lucía; Rubín Roger, Guillermo; Torres, Jesús; Bagán, Leticia; Hernández, Gonzalo; López-Pintor, Rosa María; Instituto de Salud Carlos III (ISCIII); Agencia Estatal de InvestigaciónOral cancer is a frequently malignant tumor that can be detected during an oral examination. Unfortunately, it is often diagnosed in advanced stages, which leads to low survival rates of about 50% at five years. Due to the low survival rate, it is crucial to develop automated systems that allow the classification of oral lesions according to their severity, aiding in the early diagnosis of oral cancer. This study aims to investigate the effectiveness of using clinical images and deep learning based models to perform a multiclass classification of oral mucosal lesions in color photographs taken without following any acquisition protocol. The classification differentiated four classes: malignant, potentially malignant, benign and healthy. The dataset included a total of 3246 images from 1013 patients, with 40 different categories of oral lesions, including healthy oral mucosa. The images showed different areas of the oral cavity and were captured from different perspectives by diverse dentists and maxillofacial surgeons in the practice. For the classification, different deep learning architectures were applied and compared, from the best known convolutional neural networks (CNN) and skip connection networks (SCN), to more innovative architectures such as visual transformers and a recent hybrid architecture, ConvNeXt v2. The ConvNeXt v2 Tiny architecture, with 85.53% accuracy, 85.02% precision, 85.50% recall, 84.92% F1-score, and 97.40% ROC AUC for an input image size of 354 × 354 pixels, outperformed the other architectures on the same database. The present model improved on previous proposals by considering a greater number of oral lesions and output classes.Publicación A benchmark for Rey-Osterrieth complex figure test automatic scoring(2024-10-29) Guerrero Martín, Juan; Díaz Mardomingo, María del Carmen; García Herranz, Sara; Martínez Tomás, Rafael; Rincón Zamorano, MarianoThe Rey–Osterrieth complex figure (ROCF) test is a neuropsychological task that can be useful for early detection of cognitive decline in the elderly population. Several computer vision systems have been proposed to automate this complex analysis task, but the lack of public benchmarks does not allow a fair comparison of these systems. To advance in that direction, we present a benchmarking framework for the automatic scoring of the ROCF test that provides: the ROCFD528 dataset, which is the first open dataset of ROCF line drawings; and experimental results obtained by several modern deep learning models, which can be used as a baseline for comparing new proposals. We evaluate different state-of-the-art convolutional neural networks (CNNs) under traditional and transfer learning paradigms. Experimental quantitative results (MAE = 3.448) indicate that a CNN specifically designed for sketches outperforms other state of the art CNN architectures when the number of examples available is limited. This benchmark can also be a paradigmatic example within the broad field of machine learning for the development of efficient and robust models for analyzing line drawings and sketches not only in classification but also in regression tasks.Publicación Early detection of mild cognitive impairment through neuropsychological tests in population screenings: a decision support system integrating ontologies and machine learning(Frontiers Media, 2024-10-16) Gómez-Valades Batanero, Alba; Martínez Tomás, Rafael; García Herranz, Sara; Bjørnerud, Atle; Rincón Zamorano, MarianoMachine learning (ML) methodologies for detecting Mild Cognitive Impairment (MCI) are progressively gaining prevalence to manage the vast volume of processed information. Nevertheless, the black-box nature of ML algorithms and the heterogeneity within the data may result in varied interpretations across distinct studies. To avoid this, in this proposal, we present the design of a decision support system that integrates a machine learning model represented using the Semantic Web Rule Language (SWRL) in an ontology with specialized knowledge in neuropsychological tests, the NIO ontology. The system’s ability to detect MCI subjects was evaluated on a database of 520 neuropsychological assessments conducted in Spanish and compared with other well-established ML methods. Using the F2 coefficient to minimize false negatives, results indicate that the system performs similarly to other well-established ML methods (F2TE2=0.830, only below bagging, F2BAG=0.832) while exhibiting other significant attributes such as explanation capability and data standardization to a common framework thanks to the ontological part. On the other hand, the system’s versatility and ease of use were demonstrated with three additional use cases: evaluation of new cases even if the acquisition stage is incomplete (the case records have missing values), incorporation of a new database into the integrated system, and use of the ontology capabilities to relate different domains. This makes it a useful tool to support physicians and neuropsychologists in population-based screenings for early detection of MCI.Publicación A block-based model for monitoring of human activity(Elsevier, 2011-03) Folgado Zuñiga, Encarnación; Rincón Zamorano, Mariano; Bachiller Mayoral, Margarita; Carmona Suárez, Enrique J.The study of human activity is applicable to a large number of science and technology fields, such as surveillance, biomechanics or sports applications. This article presents BB6-HM, a block-based human model for real-time monitoring of a large number of visual events and states related to human activity analysis, which can be used as components of a library to describe more complex activities in such important areas as surveillance, for example, luggage at airports, clients’ behaviour in banks and patients in hospitals. BB6-HM is inspired by the proportionality rules commonly used in Visual Arts, i.e., for dividing the human silhouette into six rectangles of the same height. The major advantage of this proposal is that analysis of the human can be easily broken down into regions, so that we can obtain information of activities. The computational load is very low, so it is possible to define a very fast implementation. Finally, this model has been applied to build classifiers for the detection of primitive events and visual attributes using heuristic rules and machine learning techniques.Publicación Identification of the optic nerve head with genetic algorithms(Elsevier, 2008-07) García Feijoó, Julián; Martínez de la Casa, José M.; Rincón Zamorano, Mariano; Carmona Suárez, Enrique J.Objective This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms. Methods and material Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an eye fundus colour image as input, a set of hypothesis points was obtained that exhibited geometric properties and intensity levels similar to the ONH contour pixels. Next, a genetic algorithm was used to find an ellipse containing the maximum number of hypothesis points in an offset of its perimeter, considering some constraints. The ellipse thus obtained is the approximation to the ONH. The segmentation method is tested in a sample of 110 eye fundus images, belonging to 55 patients with glaucoma (23.1%) and eye hypertension (76.9%) and random selected from an eye fundus image base belonging to the Ophthalmology Service at Miguel Servet Hospital, Saragossa (Spain). Results and conclusions The results obtained are competitive with those in the literature. The method's generalization capability is reinforced when it is applied to a different image base from the one used in our study and a discrepancy curve is obtained very similar to the one obtained in our image base. In addition, the robustness of the method proposed can be seen in the high percentage of images obtained with a discrepancy δ < 5 (96% and 99% in our and a different image base, respectively). The results also confirm the hypothesis that the ONH contour can be properly approached with a non-deformable ellipse. Another important aspect of the method is that it directly provides the parameters characterising the shape of the papilla: lengths of its major and minor axes, its centre of location and its orientation with regard to the horizontal position.Publicación Mild Cognitive Impairment Detection from Rey-Osterrieth Complex Figure Copy Drawings Using a Contrastive Loss Siamese Neural Network(Tech Science Press, 2025-10-15) Guerrero Martín, Juan; Estella Nonay, Eladio; Bachiller Mayoral, Margarita; Rincón Zamorano, Mariano; Universidad Nacional de Educación a Distancia (UNED)Neuropsychological tests, such as the Rey-Osterrieth complex figure (ROCF) test, help detect mild cognitive impairment (MCI) in adults by assessing cognitive abilities such as planning, organization, and memory. Furthermore, they are inexpensive and minimally invasive, making them excellent tools for early screening. In this paper, we propose the use of image analysis models to characterize the relationship between an individual’s ROCF drawing and their cognitive state.This task is usually framed as a classification problem and is solved using deep learning models, due to their success in the last decade. In order to achieve good performance, these models need to be trained with a large number of examples. Given that our data availability is limited, we alternatively treat our task as a similarity learning problem, performing pairwise ROCF drawing comparisons to define groups that represent different cognitive states.This way of working could lead to better data utilization and improved model performance. To solve the similarity learning problem, we propose a siamese neural network (SNN) that exploits the distances of arbitrary ROCF drawings to the ideal representation of the ROCF. Our proposal is compared against various deep learning models designed for classification using a public dataset of 528 ROCF copy drawings, which are associated with either healthy individuals or those with MCI. Quantitative results are derived from a scheme involving multiple rounds of evaluation, employing both a dedicated test set and 14-fold cross-validation. Our SNN proposal demonstrates superiority in validation performance, and test results comparable to those of the classification-based deep learning models.Publicación Preclinical Cognitive Markers of Alzheimer Disease and Early Diagnosis Using Virtual Reality and Artificial Intelligence: Literature Review(JMIR Publications, 2025-01-28) Scribano Parada, María de la Paz; González Palau, Fátima; Valladares Rodríguez, Sonia; Rincón Zamorano, Mariano; Rico Barroeta, Maria José; García Rodriguez, Marta; Bueno Aguado, Yolanda; Herrero Blanco, Ana; Díaz López, Estela; Bachiller Mayoral, Margarita; Losada Durán, Raquel; Agencia Estatal de InvestigaciónBackground This review explores the potential of virtual reality (VR) and artificial intelligence (AI) to identify preclinical cognitive markers of Alzheimer disease (AD). By synthesizing recent studies, it aims to advance early diagnostic methods to detect AD before significant symptoms occur. Objective Research emphasizes the significance of early detection in AD during the preclinical phase, which does not involve cognitive impairment but nevertheless requires reliable biomarkers. Current biomarkers face challenges, prompting the exploration of cognitive behavior indicators beyond episodic memory. Methods Using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched Scopus, PubMed, and Google Scholar for studies on neuropsychiatric disorders utilizing conversational data. Results Following an analysis of 38 selected articles, we highlight verbal episodic memory as a sensitive preclinical AD marker, with supporting evidence from neuroimaging and genetic profiling. Executive functions precede memory decline, while processing speed is a significant correlate. The potential of VR remains underexplored, and AI algorithms offer a multidimensional approach to early neurocognitive disorder diagnosis. Conclusions Emerging technologies like VR and AI show promise for preclinical diagnostics, but thorough validation and regulation for clinical safety and efficacy are necessary. Continued technological advancements are expected to enhance early detection and management of AD.Publicación Integrative Base Ontology for the Research Analysis of Alzheimer’s Disease-Related Mild Cognitive Impairment(Frontiers, 2021-02-04) Gómez-Valades Batanero, Alba; Martínez Tomás, Rafael; Rincón Zamorano, MarianoEarly detection of mild cognitive impairment (MCI) has become a priority in Alzheimer’s disease (AD) research, as it is a transitional phase between normal aging and dementia. However, information on MCI and AD is scattered across different formats and standards generated by different technologies, making it difficult to work with them manually. Ontologies have emerged as a solution to this problem due to their capacity for homogenization and consensus in the representation and reuse of data. In this context, an ontology that integrates the four main domains of neurodegenerative diseases, diagnostic tests, cognitive functions, and brain areas will be of great use in research. Here, we introduce the first approach to this ontology, the Neurocognitive Integrated Ontology (NIO), which integrates the knowledge regarding neuropsychological tests (NT), AD, cognitive functions, and brain areas. This ontology enables interoperability and facilitates access to data by integrating dispersed knowledge across different disciplines, rendering it useful for other research groups. To ensure the stability and reusability of NIO, the ontology was developed following the ontology-building life cycle, integrating and expanding terms from four different reference ontologies. The usefulness of this ontology was validated through use-case scenarios.Publicación Designing an effective semantic fluency test for early MCI diagnosis with machine learning(Elsevier, 2024-08-16) Gómez-Valades Batanero, Alba; Martínez Tomás, Rafael; Rincón Zamorano, MarianoSemantic fluency tests are one of the key tests used in batteries for the early detection of Mild Cognitive Impairment (MCI) as the impairment in speech and semantic memory are among the first symptoms, attracting the attention of a large number of studies. Several new semantic categories and variables capable of providing complementary information of clinical interest have been proposed to increase their effectiveness. However, this also extends the time required to complete all tests and get the overall diagnosis. Therefore, there is a need to reduce the number of tests in the batteries and thus the time spent on them while maintaining or increasing their effectiveness. This study used machine learning methods to determine the smallest and most efficient combination of semantic categories and variables to achieve this goal. We utilized a database containing 423 assessments from 141 subjects, with each subject having undergone three assessments spaced approximately one year apart. Subjects were categorized into three diagnostic groups: Healthy (if diagnosed as healthy in all three assessments), stable MCI (consistently diagnosed as MCI), and heterogeneous MCI (when exhibiting alternations between healthy and MCI diagnoses across assessments). We obtained that the most efficient combination to distinguish between these categories of semantic fluency tests included the animals and clothes semantic categories with the variables corrects, switching, clustering, and total clusters. This combination is ideal for scenarios that require a balance between time efficiency and diagnosis capability, such as population-based screenings.Publicación Decolorization with Warmth–Coolness Adjustment in an Opponent and Complementary Color System(MDPI, 2025-06-18) Sanchez Cesteros, Oscar; Rincón Zamorano, Mariano; Instituto de Salud Carlos III (ISCIII)Creating grayscale images from a color reality has been an inherent human practice since ancient times, but it became a technological challenge with the advent of the first black-and-white televisions and digital image processing. Decolorization is a process that projects visual information from a three-dimensional feature space to a onedimensional space, thus reducing the dimensionality of the image while minimizing the loss of information. To achieve this, various strategies have been developed, including the application of color channel weights and the analysis of local and global image contrast, but there is no universal solution. In this paper, we propose a bio-inspired approach that combines findings from neuroscience on the architecture of the visual system and color coding with evidence from studies in the psychology of art. The goal is to simplify the decolorization process and facilitate its control through color-related concepts that are easily understandable to humans. This new method organizes colors in a scale that links activity on the retina with a system of opponent and complementary channels, thus allowing the adjustment of the perception of warmth and coolness in the image. The results show an improvement in chromatic contrast, especially in the warmth and coolness categories, as well as an enhanced ability to preserve subtle contrasts, outperforming other approaches in the Ishihara test used in color blindness detection. In addition, the method offers a computational advantage by reducing the process through direct pixel-level operation.