Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs

Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2-a deep learning algorithm that can both detect and classify an object at the same time-on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no cyst. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.ope

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

Deep Learning Techniques for Medical Image Classification

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsIn recent years, artificial intelligence (AI) has been applied in many fields to address complex and critical real-world tasks. Deep learning rises as a subfield of AI, where artificial neural networks (ANN) are used to map complicated functions, which can be challenging even for experienced users. One of the ANN variants is called convolutional neural network (CNN), which has shown great potential in image processing by providing state-of-the-art results for many significant image processing challenges. The medical field can significantly benefit from AI usage, especially in the medical image classification domain. In this doctoral dissertation, we applied different AI techniques to analyze medical images and to give the physicians a second opinion or reduce the time and effort needed for the image classification. Initially, we reviewed several studies that were published to discuss the transfer learning of CNNs. Afterward, we studied different hyperparameters that need to be optimized for CNNs to be trained accurately. Lastly, we proposed a novel CNN architecture to help in the classification of histopathology images

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Wind farm power output prediction based on machine learning recurrent neural networks

Scientists, investors and policy makers have become aware of the importance of providing near accurate prediction of renewable energy. This is why current studies show improvements in methodologies to provide more precise energy predictions. Wind energy is tied to variabilities of weather patterns, especially wind speeds, which are irregular in climates with erratic weather conditions. To predict wind power output, model technologies like autoregressive integrated moving average (ARIMA), variants of ARIMA, hybrid models involving ARIMA and artificial neural networks (ANN), Kalman filters and support vector regressions (SVR) have been applied for wind speed involving short, ultra-short, medium and long terms kind of predictions. ARIMA ensemble with ANN has shown better performance for short and ultra-short terms of two to three hours ahead. On the other hand, SVR, Kalman filters and ensemble of both has recorded good performance for medium-term kinds of wind speed predictions. Recently, neural networks in particular recurrent neural networks (RNN) have reported immense achievement in time series predictions particularly for medium and long-term. This is largely due to its retentive memory-mapping capabilities in fitting sequence in series. These capabilities are short-lived; when the sequence grows over time, the RNN tend to lose correlated information on back-propagation operations. This can lead to errors in the predicted potentials. Therefore, RNNs are exploited for enhanced wind-farm power output prediction. The main contribution of this research is the study of a model involving a combination of RNN regularisation methods using dropout and long short-term memory (LSTM) for wind-power output predictions. In this research, the regularisation method modifies and adapts to the stochastic nature of the wind and is optimised for the wind-farm power output (WFPO) prediction for up to 12-hours ahead – 72-timesteps. This algorithm implements a dropout method to suit the non-deterministic wind speed by applying LSTM to prevent RNN from overfitting. A demonstration for accuracy using the proposed method is performed on a 14-turbine wind farm with up to ten thousand wind samples for model training and five hundred for model validation and testing. The model out performs the ARIMA model with up to 90% accuracy and is expected to be applied to erratic weather condition, especially those observed in an off-shore wind farms

Arquitectura de percepción bioinspirada basada en atención para un robot social

La atención desempeña un papel fundamental, tanto para los seres humanos como para los sistemas artificiales, ya que es una habilidad crucial que nos permite interactuar de manera efectiva con nuestro entorno. Desde la infancia hasta la edad adulta, la atención nos ayuda a concentrarnos en estímulos relevantes, procesar información de manera eficiente y responder a estímulos emocionales y sociales. Además, de influir en aspectos importantes de nuestras vidas, como el aprendizaje y las interacciones sociales. La implementación de mecanismos de atención en sistemas artificiales tiene como objetivo aprovechar los beneficios de esta habilidad fundamental. Esto se traduce en una mejora en el procesamiento de información, la toma de decisiones y la interacción con el entorno. La atención en sistemas artificiales es un área de investigación en constante desarrollo, con el propósito de mejorar la capacidad de los sistemas inteligentes en diversas aplicaciones. Uno de los campos donde más se ha estudiado el concepto de la atención es en visión artificial, en la cual se utiliza para resaltar regiones relevantes en las imágenes, lo que mejora el análisis y el reconocimiento de objetos, mientras que en la robótica, la atención permite a los robots enfocarse en objetos o eventos específicos, mejorando su capacidad de reacción y ejecución de tareas. Por este motivo, en este trabajo se propone un sistema de percepción bioinspirado basado en atención diseñado para mejorar la interacción humano-robot. Este sistema está diseñado para localizar el foco de atención del robot en cada momento teniendo en cuenta la tarea actual, los estímulos disponibles y el estado interno del robot. El sistema integra fenómenos bioinspirados como la inhibición al retorno, la relocalización del foco de atención dependiendo de los estímulos, los conceptos de atención sostenida y puntual para el cambio en el foco de atención y de agregación de estímulos de forma exógena y endógena de forma independiente. Además, se ha integrado en una plataforma robótica y se ha validado su funcionamiento en diferentes aplicaciones. Este trabajo se ha abordado desde dos perspectivas: la ampliación de las capacidades perceptuales del robot y la mejora de la interacción gracias a la integración de la atención en la arquitectura software de las plataformas robóticas. Para ello, en este trabajo se han investigado los estímulos más relevantes para la atención en humanos y su integración en el ámbito de la robótica y como realizar la agregación y fusión multisensorial de estos desde un punto de vista basado en la atención, consiguiendo una representación del entorno y seleccionando la posición del foco de atención en cada momento. Por otro lado, se ha investigado la relevancia de la integración de este sistema artificial a una plataforma robótica en lo que respecta a la interacción humano-robot, lo que ha dado lugar a un estudio que explora esta idea.Attention plays a fundamental role for both humans and artificial systems, as it is a crucial skill that enables us to interact effectively with our environment. From childhood to adulthood, attention helps us to focus on relevant stimuli, process information efficiently, and respond to emotional and social stimuli. It also influences important aspects of our lives, such as learning and social interactions. The implementation of attention mechanisms in artificial systems aims to take advantage of the benefits of this fundamental ability. This translates into improved information processing, decision making and interaction with the environment. Attention in artificial systems is an area of research in constant development, with the purpose of improving the capacity of intelligent systems in various applications. The fields where the concept of attention has been most studied are computer vision and robotics. In computer vision, attention is used to highlight relevant areas in images, which improves object analysis and recognition, while in robotics, attention allows robots to focus on specific objects or events, improving their ability to react and perform tasks. For this reason, this work proposes a bio-inspired attention-based perception system designed to improve human-robot interaction. This system is designed to locate the focus of attention of the robot at each moment, taking into account the current task, the available stimuli and the internal state of the robot.Moreover, the architecture integrates bioinspired concepts such as return inhibition, stimulus-dependent relocation of the focus of attention, the concepts of sustained and punctual attention for the shift in the focus of attention and the aggregation of exogenous and endogenous stimuli independently are integrated. In addition to this, it has been integrated into a robotic platform, and its performance has been validated in different applications. This work has been approached from two perspectives: the increase of the perceptual capabilities of the robot and the improvement of the interaction thanks to the integration of attention in the software architecture of robotic platforms. To this end, in this work, we have investigated the most relevant stimuli for attention in humans and their integration in the robotics environment, and how to perform the aggregation and multisensory fusion of these from an attention-based point of view, achieving a representation of the environment and selecting the position of the focus of attention at each moment. On the other hand, we have investigated the relevance of the integration of this artificial system to a robotic platform in terms of human-robot interaction, leading to a study that explores this idea.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Antonio Fernández Caballero.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Plinio Moreno Lópe