Persona: Bachiller Mayoral, Margarita
Cargando...
Dirección de correo electrónico
marga@dia.uned.es
ORCID
0000-0001-9122-0858
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Bachiller Mayoral
Nombre de pila
Margarita
Nombre
5 resultados
Resultados de la búsqueda
Mostrando 1 - 5 de 5
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 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 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 On the effect of feedback in multilevel representation spaces for visual surveillance tasks(Elsevier, 2009-01) Martínez Campos, Javier; Mira Mira, José; Rincón Zamorano, Mariano; Bachiller Mayoral, Margarita; Martínez Tomás, Rafael; Carmona Suárez, Enrique J.In this work we propose a general top–down feedback scheme between adjacent description levels to interpret video sequences. This scheme distinguishes two types of feedback: repair-oriented feedback and focus-oriented feedback. With the first it is possible to improve the system's performance and produce more reliable and consistent information, and with the second it is possible to adjust the computational load to match the aims. Finally, the general feedback scheme is used in different examples for a visual surveillance application which improved the final result of each description level by using the information in the higher adjacent level.