[ES] Las imágenes de fondo de ojo son muy utilizadas por los oftalmólogos para la evaluación de la retina y la detección de glaucoma. Esta patología es la segunda causa de ceguera en el mundo, según estudios de la Organización Mundial de la Salud (OMS). En esta tesis doctoral, se estudian algoritmos de aprendizaje automático (machine learning) para la evaluación automática del glaucoma usando imágenes de fondo de ojo. En primer lugar, se proponen dos métodos para la segmentación automática. El primer método utiliza la transformación Watershed Estocástica para segmentar la copa óptica y posteriormente medir características clínicas como la relación Copa/Disco y la regla ISNT. El segundo método es una arquitectura U-Net que se usa específicamente para la segmentación del disco óptico y la copa óptica. A continuación, se presentan sistemas automáticos de evaluación del glaucoma basados en redes neuronales convolucionales (CNN por sus siglas en inglés). En este enfoque se utilizan diferentes modelos entrenados en ImageNet como clasificadores automáticos de glaucoma, usando fine-tuning. Esta nueva técnica permite detectar el glaucoma sin segmentación previa o extracción de características. Además, este enfoque presenta una mejora considerable del rendimiento comparado con otros trabajos del estado del arte. En tercer lugar, dada la dificultad de obtener grandes cantidades de imágenes etiquetadas (glaucoma/no glaucoma), esta tesis también aborda el problema de la síntesis de imágenes de la retina. En concreto se analizaron dos arquitecturas diferentes para la síntesis de imágenes, las arquitecturas Variational Autoencoder (VAE) y la Generative Adversarial Networks (GAN). Con estas arquitecturas se generaron imágenes sintéticas que se analizaron cualitativa y cuantitativamente, obteniendo un rendimiento similar a otros trabajos en la literatura. Finalmente, en esta tesis se plantea la utilización de un tipo de GAN (DCGAN) como alternativa a los sistemas automáticos de evaluación del glaucoma presentados anteriormente. Para alcanzar este objetivo se implementó un algoritmo de aprendizaje semi-supervisado. / [CA] Les imatges de fons d'ull són molt utilitzades pels oftalmòlegs per a l'avaluació de la retina i la detecció de glaucoma. Aquesta patologia és la segona causa de ceguesa al món, segons estudis de l'Organització Mundial de la Salut (OMS). En aquesta tesi doctoral, s'estudien algoritmes d'aprenentatge automàtic (machine learning) per a l'avaluació automàtica del glaucoma usant imatges de fons d'ull. En primer lloc, es proposen dos mètodes per a la segmentació automàtica. El primer mètode utilitza la transformació Watershed Estocàstica per segmentar la copa òptica i després mesurar característiques clíniques com la relació Copa / Disc i la regla ISNT. El segon mètode és una arquitectura U-Net que s'usa específicament per a la segmentació del disc òptic i la copa òptica. A continuació, es presenten sistemes automàtics d'avaluació del glaucoma basats en xarxes neuronals convolucionals (CNN per les sigles en anglès). En aquest enfocament s'utilitzen diferents models entrenats en ImageNet com classificadors automàtics de glaucoma, usant fine-tuning. Aquesta nova tècnica permet detectar el glaucoma sense segmentació prèvia o extracció de característiques. A més, aquest enfocament presenta una millora considerable del rendiment comparat amb altres treballs de l'estat de l'art. En tercer lloc, donada la dificultat d'obtenir grans quantitats d'imatges etiquetades (glaucoma / no glaucoma), aquesta tesi també aborda el problema de la síntesi d'imatges de la retina. En concret es van analitzar dues arquitectures diferents per a la síntesi d'imatges, les arquitectures Variational Autoencoder (VAE) i la Generative adversarial Networks (GAN). Amb aquestes arquitectures es van generar imatges sintètiques que es van analitzar qualitativament i quantitativament, obtenint un rendiment similar a altres treballs a la literatura. Finalment, en aquesta tesi es planteja la utilització d'un tipus de GAN (DCGAN) com a alternativa als sistemes automàtics d'avaluació del glaucoma presentats anteriorment. Per assolir aquest objectiu es va implementar un algoritme d'aprenentatge semi-supervisat. / [EN] Fundus images are widely used by ophthalmologists to assess the retina and detect glaucoma, which is, according to studies from the World Health Organization (WHO), the second cause of blindness worldwide. In this thesis, machine learning algorithms for automatic glaucoma assessment using fundus images are studied. First, two methods for automatic segmentation are proposed. The first method uses the Stochastic Watershed transformation to segment the optic cup and measures clinical features such as the Cup/Disc ratio and ISNT rule. The second method is a U-Net architecture focused on the optic disc and optic cup segmentation task. Secondly, automated glaucoma assessment systems using convolutional neural networks (CNNs) are presented. In this approach, different ImageNet-trained models are fine-tuned and used as automatic glaucoma classifiers. These new techniques allow detecting glaucoma without previous segmentation or feature extraction. Moreover, it improves the performance of other state-of-art works. Thirdly, given the difficulty of getting large amounts of glaucoma-labelled images, this thesis addresses the problem of retinal image synthesis. Two different architectures for image synthesis, the Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) architectures, were analysed. Using these models, synthetic images that were qualitative and quantitative analysed, reporting state-of-the-art performance, were generated. Finally, an adversarial model is used to create an alternative automatic glaucoma assessment system. In this part, a semi-supervised learning algorithm was implemented to reach this goal. / The research derived from this doctoral thesis has been supported by the Generalitat Valenciana under the scholarship Santiago Grisolía [GRISOLIA/2015/027]. / Díaz Pinto, AY. (2019). Machine Learning for Glaucoma Assessment using Fundus Images [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/124351 / TESIS

<p dir="ltr">Both diet and physical activity are known to be risk factors for obesity and chronic diseases such as diabetes and metabolic syndrome. We explore a distance-based approach for clustering daily physical activity time series to find temporal physical activity patterns among U.S. adults (ages 20-65). We further extend this approach to integrate both diet and physical activity, and find joint temporal diet and physical activity patterns. Our experiments indicate that the integration of diet, physical activity, and time has the potential to discover joint patterns with association to health. </p><p dir="ltr">Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to learn identity information from labeled images in source domains and apply it to unlabeled images in a target domain. We propose a deep learning architecture called Synthesis Model Bank (SMB) to deal with illumination variation in unsupervised person re-ID. From our experiments, the proposed SMB outperforms other synthesis methods on several re-ID benchmarks. </p><p dir="ltr">Recent technology advancement introduced modern high-throughput methodologies such as Unmanned Aerial Vehicles (UAVs) to replace the traditional, labor-intensive phenotyping. For many UAV phenotyping analysis, the first step is to extract the smallest groups of plants called “plots” that have the same genotype. We propose an optimization-based, rotation-adaptive approach for extracting plots in a UAV RGB orthomosaic image. From our experiments, the proposed method achieves better plot extraction accuracy compared to existing approaches, and does not require training data.</p>

Machine learning applications within medical imaging often suffer from a lack of data, as a consequence of restrictions that hinder the free distribution of patient information. In this project, GANs (generative adversarial networks) are used to generate data synthetically, in an effort to circumvent this issue. The GAN framework PGAN is trained on the brain tumor segmentation dataset BraTS to generate new, synthetic brain tumor masks with the same visual characteristics as the real samples. The image-to-image translation network SPADE is subsequently trained on the image pairs in the real dataset, to learn a transformation from segmentation masks to brain MR images, and is in turn used to map the artificial segmentation masks generated by PGAN to corresponding artificial MR images. The images generated by these networks form a new, synthetic dataset, which is used to augment the original dataset. Different quantities of real and synthetic data are then evaluated in three different brain tumor segmentation tasks, where the image segmentation network U-Net is trained on this data to segment (real) MR images into the classes in question. The final segmentation performance of each training instance is evaluated over test data from the real dataset with the Weighted Dice Loss metric. The results indicate a slight increase in performance across all segmentation tasks evaluated in this project, when including some quantity of synthetic images. However, the differences were largest when the experiments were restricted to using only 20 % of the real data, and less significant when the full dataset was made available. A majority of the generated segmentation masks appear visually convincing to an extent (although somewhat noisy with regards to the intra-tumoral classes), while a relatively large proportion appear heavily noisy and corrupted. However, the translation of segmentation masks to MR images via SPADE proved more reliable and consistent.

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