Fundus image analysis for automatic screening of ophthalmic pathologies

En los ultimos años el número de casos de ceguera se ha reducido significativamente. A pesar de este hecho, la Organización Mundial de la Salud estima que un 80% de los casos de pérdida de visión (285 millones en 2010) pueden ser evitados si se diagnostican en sus estadios más tempranos y son tratados de forma efectiva. Para cumplir esta propuesta se pretende que los servicios de atención primaria incluyan un seguimiento oftalmológico de sus pacientes así como fomentar campañas de cribado en centros proclives a reunir personas de alto riesgo. Sin embargo, estas soluciones exigen una alta carga de trabajo de personal experto entrenado en el análisis de los patrones anómalos propios de cada enfermedad. Por lo tanto, el desarrollo de algoritmos para la creación de sistemas de cribado automáticos juga un papel vital en este campo. La presente tesis persigue la identificacion automática del daño retiniano provocado por dos de las patologías más comunes en la sociedad actual: la retinopatía diabética (RD) y la degenaración macular asociada a la edad (DMAE). Concretamente, el objetivo final de este trabajo es el desarrollo de métodos novedosos basados en la extracción de características de la imagen de fondo de ojo y clasificación para discernir entre tejido sano y patológico. Además, en este documento se proponen algoritmos de pre-procesado con el objetivo de normalizar la alta variabilidad existente en las bases de datos publicas de imagen de fondo de ojo y eliminar la contribución de ciertas estructuras retinianas que afectan negativamente en la detección del daño retiniano. A diferencia de la mayoría de los trabajos existentes en el estado del arte sobre detección de patologías en imagen de fondo de ojo, los métodos propuestos a lo largo de este manuscrito evitan la necesidad de segmentación de las lesiones o la generación de un mapa de candidatos antes de la fase de clasificación. En este trabajo, Local binary patterns, perfiles granulométricos y la dimensión fractal se aplican de manera local para extraer información de textura, morfología y tortuosidad de la imagen de fondo de ojo. Posteriormente, esta información se combina de diversos modos formando vectores de características con los que se entrenan avanzados métodos de clasificación formulados para discriminar de manera óptima entre exudados, microaneurismas, hemorragias y tejido sano. Mediante diversos experimentos, se valida la habilidad del sistema propuesto para identificar los signos más comunes de la RD y DMAE. Para ello se emplean bases de datos públicas con un alto grado de variabilidad sin exlcuir ninguna imagen. Además, la presente tesis también cubre aspectos básicos del paradigma de deep learning. Concretamente, se presenta un novedoso método basado en redes neuronales convolucionales (CNNs). La técnica de transferencia de conocimiento se aplica mediante el fine-tuning de las arquitecturas de CNNs más importantes en el estado del arte. La detección y localización de exudados mediante redes neuronales se lleva a cabo en los dos últimos experimentos de esta tesis doctoral. Cabe destacar que los resultados obtenidos mediante la extracción de características "manual" y posterior clasificación se comparan de forma objetiva con las predicciones obtenidas por el mejor modelo basado en CNNs. Los prometedores resultados obtenidos en esta tesis y el bajo coste y portabilidad de las cámaras de adquisión de imagen de retina podrían facilitar la incorporación de los algoritmos desarrollados en este trabajo en un sistema de cribado automático que ayude a los especialistas en la detección de patrones anomálos característicos de las dos enfermedades bajo estudio: RD y DMAE.In last years, the number of blindness cases has been significantly reduced. Despite this promising news, the World Health Organisation estimates that 80% of visual impairment (285 million cases in 2010) could be avoided if diagnosed and treated early. To accomplish this purpose, eye care services need to be established in primary health and screening campaigns should be a common task in centres with people at risk. However, these solutions entail a high workload for trained experts in the analysis of the anomalous patterns of each eye disease. Therefore, the development of algorithms for automatic screening system plays a vital role in this field. This thesis focuses on the automatic identification of the retinal damage provoked by two of the most common pathologies in the current society: diabetic retinopathy (DR) and age-related macular degeneration (AMD). Specifically, the final goal of this work is to develop novel methods, based on fundus image description and classification, to characterise the healthy and abnormal tissue in the retina background. In addition, pre-processing algorithms are proposed with the aim of normalising the high variability of fundus images and removing the contribution of some retinal structures that could hinder in the retinal damage detection. In contrast to the most of the state-of-the-art works in damage detection using fundus images, the methods proposed throughout this manuscript avoid the necessity of lesion segmentation or the candidate map generation before the classification stage. Local binary patterns, granulometric profiles and fractal dimension are locally computed to extract texture, morphological and roughness information from retinal images. Different combinations of this information feed advanced classification algorithms formulated to optimally discriminate exudates, microaneurysms, haemorrhages and healthy tissues. Through several experiments, the ability of the proposed system to identify DR and AMD signs is validated using different public databases with a large degree of variability and without image exclusion. Moreover, this thesis covers the basics of the deep learning paradigm. In particular, a novel approach based on convolutional neural networks is explored. The transfer learning technique is applied to fine-tune the most important state-of-the-art CNN architectures. Exudate detection and localisation tasks using neural networks are carried out in the last two experiments of this thesis. An objective comparison between the hand-crafted feature extraction and classification process and the prediction models based on CNNs is established. The promising results of this PhD thesis and the affordable cost and portability of retinal cameras could facilitate the further incorporation of the developed algorithms in a computer-aided diagnosis (CAD) system to help specialists in the accurate detection of anomalous patterns characteristic of the two diseases under study: DR and AMD.En els últims anys el nombre de casos de ceguera s'ha reduït significativament. A pesar d'este fet, l'Organització Mundial de la Salut estima que un 80% dels casos de pèrdua de visió (285 milions en 2010) poden ser evitats si es diagnostiquen en els seus estadis més primerencs i són tractats de forma efectiva. Per a complir esta proposta es pretén que els servicis d'atenció primària incloguen un seguiment oftalmològic dels seus pacients així com fomentar campanyes de garbellament en centres regentats per persones d'alt risc. No obstant això, estes solucions exigixen una alta càrrega de treball de personal expert entrenat en l'anàlisi dels patrons anòmals propis de cada malaltia. Per tant, el desenrotllament d'algoritmes per a la creació de sistemes de garbellament automàtics juga un paper vital en este camp. La present tesi perseguix la identificació automàtica del dany retiniano provocat per dos de les patologies més comunes en la societat actual: la retinopatia diabètica (RD) i la degenaración macular associada a l'edat (DMAE) . Concretament, l'objectiu final d'este treball és el desenrotllament de mètodes novedodos basats en l'extracció de característiques de la imatge de fons d'ull i classificació per a discernir entre teixit sa i patològic. A més, en este document es proposen algoritmes de pre- processat amb l'objectiu de normalitzar l'alta variabilitat existent en les bases de dades publiques d'imatge de fons d'ull i eliminar la contribució de certes estructures retinianas que afecten negativament en la detecció del dany retiniano. A diferència de la majoria dels treballs existents en l'estat de l'art sobre detecció de patologies en imatge de fons d'ull, els mètodes proposats al llarg d'este manuscrit eviten la necessitat de segmentació de les lesions o la generació d'un mapa de candidats abans de la fase de classificació. En este treball, Local binary patterns, perfils granulometrics i la dimensió fractal s'apliquen de manera local per a extraure informació de textura, morfologia i tortuositat de la imatge de fons d'ull. Posteriorment, esta informació es combina de diversos modes formant vectors de característiques amb els que s'entrenen avançats mètodes de classificació formulats per a discriminar de manera òptima entre exsudats, microaneurismes, hemorràgies i teixit sa. Per mitjà de diversos experiments, es valida l'habilitat del sistema proposat per a identificar els signes més comuns de la RD i DMAE. Per a això s'empren bases de dades públiques amb un alt grau de variabilitat sense exlcuir cap imatge. A més, la present tesi també cobrix aspectes bàsics del paradigma de deep learning. Concretament, es presenta un nou mètode basat en xarxes neuronals convolucionales (CNNs) . La tècnica de transferencia de coneixement s'aplica per mitjà del fine-tuning de les arquitectures de CNNs més importants en l'estat de l'art. La detecció i localització d'exudats per mitjà de xarxes neuronals es du a terme en els dos últims experiments d'esta tesi doctoral. Cal destacar que els resultats obtinguts per mitjà de l'extracció de característiques "manual" i posterior classificació es comparen de forma objectiva amb les prediccions obtingudes pel millor model basat en CNNs. Els prometedors resultats obtinguts en esta tesi i el baix cost i portabilitat de les cambres d'adquisión d'imatge de retina podrien facilitar la incorporació dels algoritmes desenrotllats en este treball en un sistema de garbellament automàtic que ajude als especialistes en la detecció de patrons anomálos característics de les dos malalties baix estudi: RD i DMAE.Colomer Granero, A. (2018). Fundus image analysis for automatic screening of ophthalmic pathologies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/99745TESI

An approach to developing a neurophysiological index capable of predicting a student's cognitive performance from classroom design

[EN] Classroom design influences cognitive functions such as memory and attention. This relationship between environment and performance is complex, and the cognitive and neurophysiological effects are closely intertwined. The aim of this paper is to lay the foundations for a methodology capable of assessing the impact of classroom design on students' attention and memory, using automated cognitive indices based on neurophysiological measures. To this end, a laboratory study was carried out in which 50 subjects performed cognitive tests in virtual environments with different design configurations. During the tests, their psychological responses (attention and memory performance) and neurophysiological responses (electrocardiogram, electroencephalogram, and electrodermal response) were recorded. After processing the signals and extracting different metrics, correlations between the two types of responses were studied. This provided a basis of relationships for the future choice of metrics with which to train predictive models. The use of Artificial Intelligence will make it possible to automatically quantify the impact of classroom design on students' attention and memory, using indices based on neurophysiological measures.[ES] El diseño del aula influye en las funciones cognitivas, como la memoria y la atención. Esta relación entre entorno y rendimiento es compleja, y los efectos cognitivos y neurofisiológicos están estrechamente entrelazados. El objetivo del presente trabajo es centrar las bases de una metodología capaz de evaluar el impacto del diseño del aula en la atención y memoria de los alumnos, a partir de índices cognitivos automatizados basados en medidas neurofisiológicas. Para ello se llevó a cabo un estudio en laboratorio, en el que 50 sujetos realizaron pruebas cognitivas en entornos virtuales con diferentes configuraciones de diseño. Durante estas se registraron sus respuestas psicológicas (relativas al rendimiento en atención y memoria) y respuestas neurofisiológicas (electrocardiograma, electroencefalograma, y respuesta electrodérmica). Tras el tratamiento de las señales y la extracción de distintas métricas, se estudiaron las correlaciones entre ambos tipos de respuestas. Esto ofreció una base de relaciones para la futura elección de métricas con las que entrenar modelos predictivos. El uso de Inteligencia Artificial permitirá cuantificar de manera automática el impacto del diseño del aula en la atención y memoria de los alumnos, a partir de índices basados en medidas neurofisiológicas. Desarrollo con distintas aplicaciones en el área de la educación.Este trabajo ha sido financiado por el Ministerio de Economía, Industria y Competitividad de España (Proyecto BIA2017-86157-R; PRE2018-084051).Higuera Trujillo, JL.; Colomer Granero, A.; Naranjo Ornedo, V.; Llinares Millan, C. (2021). Una aproximación al desarrollo de un índice neurofisiológico capaz de predecir el rendimiento cognitivo de un alumno a partir del diseño del aula. Editorial Universitat Politècnica de València. 619-618. https://doi.org/10.4995/EDIFICATE2021.2021.13578OCS61961

Aircraft Dynamic Rerouting Support

In the frame of Clean Sky 2 JU, the HARVIS (Human Aircraft Roadmap for Virtual Intelligent System) project introduces a cockpit assistant committed to help the pilot to reroute the aircraft in single-pilot operations. A relevant scenario for this AI assistant is that in which diversion to alternate airfield is required after an emergency. Another interesting scenario is the anticipation of radar vectors in the arrivals with time enough to safely configure the aircraft for the descent. A demonstrator is being developed for this second scenario in the context of Project HARVIS (www.harvis-project.eu). Diversion is often required after system failure, medical emergency, or just for weather phenomena (dense fog, storms, etc.) in the approaching. During regular operation if a diversion is needed the pilot in command and first officer discuss on the multiple options they have and try to find out the one they think is the best. The AI assistant will take into account characteristics of nearby airports, METAR at destination, and facilities to take care of passengers, among other factors. It may then consider several options, assess the risks and benefits of each one, and finally inform the pilot accordingly. In this scenario, the digital assistant takes care of the Options and Risks in a FORDEC procedure

Impulsive Personality Traits Predicted Weight Loss in Individuals with Type 2 Diabetes after 3 Years of Lifestyle Interventions

Impulsivity has been associated with type 2 diabetes (T2D) and may negatively impact its management. This study aimed to investigate impulsive personality traits in an older adult population with T2D and their predicting role in long-term weight control and glycemic management, through glycated hemoglobin (HbA(1c)), following 3 years of intervention with a Mediterranean diet. The Impulsive Behavior Scale (UPPS-P) was administered as a measure of impulsive traits at baseline. Results showed higher total baseline scores of UPPS-P, and higher positive urgency in individuals with T2D, compared with those without T2D. The regression analysis in patients with T2D showed that sensation seeking and lack of perseverance predicted weight loss at follow-up. By contrast, impulsive traits did not predict follow-up levels of HbA(1c). In conclusion, the present findings suggest that higher impulsive traits in individuals with T2D seem to affect long-term weight control, but not glycemic control

Toward a Non Stabilized Approach assistant based on human expertise

97% of Non-Stabilized Approach (NSA) are continued until landing going against Standard Operational Procedures (SOP). For some of these approaches, the reason is a lack of situation awareness for others it is because of operational constraints that standard SOP do not take into account like ATC, remaining fuel on board, weather… Most of the time everything goes well but pilots often admit afterwards that they should have go-around and that safety margins were greatly reduced

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

[EN] The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.This work has been supported by the Heineken Endowed Chair in Neuromarketing at the Polytechnic University of Valencia in order to research and apply new technologies and neuroscience in communication, distribution and consumption fields.Guixeres Provinciale, J.; Bigné-Alcañiz, E.; Ausin-Azofra, JM.; Alcañiz Raya, ML.; Colomer, A.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V. (2017). Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising. 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A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permissionThis work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a 30-min audiovisual content. This content was composed by a submarine documentary and nine commercials (one of themthe ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and RandomForest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing.This work has been supported by the Heineken Endowed Chair in Neuromarketing at the Universitat Politecnica de Valencia in order to research and apply new technologies and neuroscience in communication, distribution and consumption fields.Colomer Granero, A.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Guixeres Provinciale, J.; Ausin-Azofra, JM.; Alcañiz Raya, ML. (2016). A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Frontiers in Computational Neuroscience. 10(74):1-16. doi:10.3389/fncom.2016.00074S116107

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

[EN] Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with nonimmersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject's arousal and valence perception. The model's accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.This work was supported by the Ministerio de Economia y Competitividad. Spain (Project TIN2013-45736-R).Marín-Morales, J.; Higuera-Trujillo, JL.; Greco, A.; Guixeres Provinciale, J.; Llinares Millán, MDC.; Scilingo, EP.; Alcañiz Raya, ML.... (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports. 8:1-15. https://doi.org/10.1038/s41598-018-32063-4S1158Picard, R. W. Affective computing. (MIT press, 1997).Picard, R. W. Affective Computing: Challenges. Int. J. Hum. Comput. 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