Persona: Cuadra Troncoso, José Manuel
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jmcuadra@dia.uned.es
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0000-0003-3616-0404
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Cuadra Troncoso
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José Manuel
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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 Modelado adaptativo del medio para la navegación de robots autónomos utilizando algoritmos basados en el centro de áreas(Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial, 2011-05-09) Cuadra Troncoso, José Manuel; Álvarez Sánchez, José Ramón; Paz López, Félix de laEsta tesis tiene como objetivo la investigación de nuevos métodos de navegación reactiva para robots autónomos, considerada como base de cualquier otra forma de navegación de más alto nivel. En la navegación reactiva el robot se mueve en función únicamente de lo que percibe, no hay una planificación previa de la ruta a seguir. El tipo de robot que consideraremos en este trabajo es el robot terrestre equipado con sensores de rango 2D. Estos sensores proporcionan al robot mediciones de las distancias a las que se encuentran los objetos que le rodean, rescindiendo dichas mediciones a un plano paralelo al suelo. Estamos por lo tanto, en un contexto de movimientos y mediciones bidimensionales.Publicación ComputationalapproachestoExplainableArtificial Intelligence: Advances in theory, applications and trends(Elsevier, 2023-12-01) Górriz, J. M.; Álvarez Illán, I.; Álvarez Marquina, A.; Arco, J.E.; Atzmueller, M.; Ballarini, F.; Barakova, Emilia; Bologna, Guido; Bonomini, P.; Castellanos Dominguez, Cesar Germán; Castillo Barnes, Diego; Cho, Sung Bae; Contreras, R.; Cuadra Troncoso, José Manuel; Domínguez, Enrique; Domínguez Mateos, F.; Duro, Richard J.; Elizondo, David A.; Fernández Caballero, Antonio; Fernández Jover, Eduardo; Ferrández Vicente, J.M.Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated humanlevel performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.Publicación Self-Learning Robot Autonomous Navigation with Deep Reinforcement Learning Techniques(MDPI, 2023-12-30) Pintos Gómez de las Heras, Borja; Martínez Tomás, Rafael; Cuadra Troncoso, José ManuelComplex and high-computational-cost algorithms are usually the state-of-the-art solution for autonomous driving cases in which non-holonomic robots must be controlled in scenarios with spatial restrictions and interaction with dynamic obstacles while fulfilling at all times safety, comfort, and legal requirements. These highly complex software solutions must cover the high variability of use cases that might appear in traffic conditions, especially when involving scenarios with dynamic obstacles. Reinforcement learning algorithms are seen as a powerful tool in autonomous driving scenarios since the complexity of the algorithm is automatically learned by trial and error with the help of simple reward functions. This paper proposes a methodology to properly define simple reward functions and come up automatically with a complex and successful autonomous driving policy. The proposed methodology has no motion planning module so that the computational power can be limited like in the reactive robotic paradigm. Reactions are learned based on the maximization of the cumulative reward obtained during the learning process. Since the motion is based on the cumulative reward, the proposed algorithm is not bound to any embedded model of the robot and is not being affected by uncertainties of these models or estimators, making it possible to generate trajectories with the consideration of non-holonomic constrains. This paper explains the proposed methodology and discusses the setup of experiments and the results for the validation of the methodology in scenarios with dynamic obstacles. A comparison between the reinforcement learning algorithm and state-of-the-art approaches is also carried out to highlight how the methodology proposed outperforms state-of-the-art algorithms.Publicación A novel method for virtual real-time cumuliform fluid dynamics simulation using deep recurrent neural networks(MDPI, 2025-08-26) Jiménez de Parga, Carlos; Calo, Sergio; Cuadra Troncoso, José Manuel; García-Vico, Ángel M.; Pastor Vargas, RafaelThe real-time simulation of atmospheric clouds for the visualisation of outdoor scenarios has been a computer graphics research challenge since the emergence of the natural phenomena rendering field in the 1980s. In this work, we present an innovative method for real-time cumuli movement and transition based on a Recurrent Neural Network (RNN). Specifically, an LSTM, a GRU and an Elman RNN network are trained on time-series data generated by a parallel Navier–Stokes fluid solver. The training process optimizes the network to predict the velocity of cloud particles for the subsequent time step, allowing the model to act as a computationally efficient surrogate for the full physics simulation. In the experiments, we obtained natural-looking behaviour for cumuli evolution and dissipation with excellent performance by the RNN fluid algorithm compared with that of classical finite-element computational solvers. These experiments prove the suitability of our ontogenetic computational model in terms of achieving an optimum balance between natural-looking realism and performance in opposition to computationally expensive hyper-realistic fluid dynamics simulations which are usually in non-real time. Therefore, the core contributions of our research to the state of the art in cloud dynamics are the following: a progressively improved real-time step of the RNN-LSTM fluid algorithm compared to the previous literature to date by outperforming the inference times during the runtime cumuli animation in the analysed hardware, the absence of spatial grid bounds and the replacement of fluid dynamics equation solving with the RNN. As a consequence, this method is applicable in flight simulation systems, climate awareness educational tools, atmospheric simulations, nature-based video games and architectural software.Publicación An Efficient and Rotation Invariant Fourier-Based Metric for Assessing the Quality of Images Created by Generative Models(Springer, 2022-06-24) Gamazo,José María; Cuadra Troncoso, José Manuel; Rincón Zamorano, Mariano; Ministerio de Ciencia e Innovación (España)Recent progress in generative image modeling is leading to a new era of high-resolution fakes visually indistinguishable from real life images. However, the development of metrics capable of discerning whether images are synthetic or not runs behind the race of achieving the best generator, thus bringing potential threats. We propose a rotation invariant metric capable of distinguishing real and generated image datasets and we call it CSD (Circular Spectrum Distance) due to its circular nature and its inherent relation to the Fourier Spectrum. Its performance is analysed on a whole brain MRI dataset. CSD has similar behavior to FID during training but requires smaller batch sizes and is faster to compute.