Historical photographs can generate significant cultural and economic value, but often their subjects go unidentified. However, if analyzed correctly, visual clues in these photographs can open up new directions in identifying unknown subjects. For example, many 19th century photographs contain painted backdrops that can be mapped to a specific photographer or location, but this research process is often manual, time-consuming, and unsuccessful. AI-based computer vision algorithms could be used to automatically identify painted backdrops or photographers or cluster photos with similar backdrops in order to aid researchers. However, it is unknown which computer vision algorithms are feasible for painted backdrop identification or which techniques work better than others. We present three studies evaluating four different types of image embeddings – Inception, CLIP, MAE, and pHash – across a variety of metrics and techniques. We find that a workflow using CLIP embeddings combined with a background classifier and simulated user feedback performs best. We also discuss implications for human-AI collaboration in visual analysis and new possibilities for digital humanities scholarship. / Master of Science / Historical photographs can generate significant cultural and economic value, but often their subjects go unidentified. However, if these photographs are analyzed correctly, clues in these photographs can open up new directions in identifying unknown subjects. For example, many 19th century photographs contain painted backdrops that can be mapped to a specific photographer or location, but this research process is often manual, time-consuming, and unsuccessful. Artificial Intelligence-based computer vision techniques could be used to automatically identify painted backdrops or photographers or group together photos with similar backdrops in order to aid researchers. However, it is unknown which computer vision techniques are feasible for painted backdrop identification or which techniques work better than others. We present three studies comparing four different types of computer vision techniques – Inception, CLIP, MAE, and pHash – across a variety of metrics. We find that a workflow that combines the CLIP computer vision technique, software that automatically classifies photo backgrounds, and simulated human feedback performs best. We also discuss implications for collaboration between humans and AI for analyzing images and new possibilities for academic research combining technology and history.

Masters of Science / Upcoming and ongoing 21cm surveys, such as the Square Kilometre Array (SKA), Hydrogen Epoch of Reionization Array (HERA) and Low Frequency Array (LOFAR), will enable imaging of the neutral hydrogen distribution on cosmological scales in the early Universe. These experiments are expected to generate huge imaging datasets that will encode more information than the power spectrum. This provides an alternative unique way to constrain the astrophysical and cosmological parameters, which might break the degeneracies in the power spectral analysis. The global history of reionization remains fairly unconstrained. In this thesis, we explore the viability of directly using the 21cm images to reconstruct and constrain the reionization history. Using Convolutional Neural Networks (CNN), we create a fast estimator of the global ionization fraction from the 21cm images as produced by our Large Semi-numerical Simulation (SimFast21). Our estimator is able to efficiently recover the ionization fraction (xHII) at several redshifts, z = 7; 8; 9; 10 with an accuracy of 99% as quantified by the coefficient of determination R2 without being given any additional information about the 21cm maps. This approach, contrary to estimations based on the power spectrum, is model independent. When adding the thermal noise and instrumental effects from these 21cm arrays, the results are sensitive to the foreground removal level, affecting the recovery of high neutral fractions. We also observe similar trend when combining all redshifts but with an improved accuracy. Our analysis can be easily extended to place additional constraints on other astrophysical parameters such as the photon escape fraction. This work represents a step forward to extract the astrophysical and cosmological information from upcoming 21cm surveys.

This thesis presents a deep learning tool able to identify ice in radar images fromthe sea-ice environment of the Twhaites glacier outlet. The project is motivatedby the threatening situation of the Thwaites glacier that has been increasingits mass loss rate during the last decade. This is of concern considering thelarge mass of ice held by the glacier, that in case of melting, could increasethe mean sea level by more than +65 cm [1]. The algorithm generated alongthis work is intended to help in the generation of navigation charts and identificationof icebergs in future stages of the project, outside of the scope of this thesis.The data used for this task are ICEYE’s X-band radar images from the Thwaitessea-ice environment, the target area to be studied. The corresponding groundtruth for each of the samples has been manually generated identifying the iceand icebergs present in each image. Additional data processing includes tiling,to increment the number of samples, and augmentation, done by horizontal andvertical flips of a random number of tiles.The proposed tool performs semantic segmentation on radar images classifyingthe class "Ice". It is developed by a deep learning Convolutional Neural Network(CNN) model, trained with prepared ICEYE’s radar images. The model reachesvalues of F1 metric higher than 89% in the images of the target area (Thwaitessea-ice environment) and is able to generalize to different regions of Antarctica,reaching values of F1 = 80 %. A potential alternative version of the algorithm isproposed and discussed. This alternative score F1 values higher than F1 > 95 %for images of the target environment and F1 = 87 % for the image of the differentregion. However, it must not be confirmed as the final algorithm due to the needfor further verification.

Deep learning has in recent years come to dominate the previously separate fields of research in machine learning, computer vision, natural language understanding and speech recognition. Despite breakthroughs in training deep networks, there remains a lack of understanding of both the optimization and structure of deep networks. The approach advocated by many researchers in the field has been to train monolithic networks with excess complexity, and strong regularization --- an approach that leaves much to desire in efficiency. Instead we propose that carefully designing networks in consideration of our prior knowledge of the task and learned representation can improve the memory and compute efficiency of state-of-the art networks, and even improve generalization --- what we propose to denote as structural priors. We present two such novel structural priors for convolutional neural networks, and evaluate them in state-of-the-art image classification CNN architectures. The first of these methods proposes to exploit our knowledge of the low-rank nature of most filters learned for natural images by structuring a deep network to learn a collection of mostly small, low-rank, filters. The second addresses the filter/channel extents of convolutional filters, by learning filters with limited channel extents. The size of these channel-wise basis filters increases with the depth of the model, giving a novel sparse connection structure that resembles a tree root. Both methods are found to improve the generalization of these architectures while also decreasing the size and increasing the efficiency of their training and test-time computation. Finally, we present work towards conditional computation in deep neural networks, moving towards a method of automatically learning structural priors in deep networks. We propose a new discriminative learning model, conditional networks, that jointly exploit the accurate representation learning capabilities of deep neural networks with the efficient conditional computation of decision trees. Conditional networks yield smaller models, and offer test-time flexibility in the trade-off of computation vs. accuracy.

In this thesis is handled the problem of mushroom detection and recognition in natural environment. Convolutional neural networks are used. The beginning of this thesis is dedicated to the theory of neural networks. Further is solved the problem of object detection and classification. Using neural network trained for classification is solved also the task of localization. Results of trained CNNs are analised.

Context : Air being the free source is used in different ways commercially. In earlier days windmills generate power, water, and electricity. The excessive establishment of windmills for commercial purposes affected avifauna. Most of the birds lost their lives due to collisions with windmills. Turbines used to generate power near airports are also one of the causes for the extinction of birdlife. According to a survey in 2011 in Canada a total of 23,300 bird deaths were caused by wind turbines and also it is estimated that the number of deaths would increase to 2,33,000 in the following 10-15 years. Objectives : The main objective of this thesis is to find a suitable software solution to detect the birds on a series of grayscale images in real-time and in minimum full HD resolution with at least a 15 FPS rate. User-Driven Design Methodology is used for developing, tools are Python and Open-CV. Methods : In this research, a system is designed to detect the bird in an HD Video. Possible methods that can be considered are convolutional neural networks (CNN), vision based detection with background subtraction, contour detection and confusion matrix classification. These methods detect birds in raw images and with help of a classifier make it possible to see the bird in desired pixels with full resolution. We will investigate a bird classification method consisting of two steps, background subtraction, and then object classification. Background subtraction is a fundamental method to extract moving objects from a fixed background. For classification, we will use the YOLO v3 model version for object classification. Results : The project is expected to result in a system design and prototype for the bird identification on a grayscale video stream in at least full HD resolution in a minimum of 15 FPS. The bird should be distinguished from other moving objects like wind turbine blades, trees, or clouds. The proposed solution should identify up to 5 birds simultaneously. Conclusion : After completing each step and arriving at the classification, the methods we have tried, such as Haar Cascades and mobile-net SSD, were not providing us with the desired results. So we opted to use YOLO v3, which had the best accuracy in classifying different objects. By using the YOLO v3 classifier, we have detected the bird with 95% accuracy, blades with 90% accuracy, clouds with 80% accuracy, trees with 70% accuracy. Moreover, we conclude that there is a need for further empirical validation of the models in full-scale industry trials.

[ES] Las enfermedades cardiovasculares son una de las causas más predominantes de muerte y comorbilidad en los países desarrollados, por ello se han realizado grandes inversiones en las últimas décadas para producir herramientas de diagnóstico y aplicaciones de tratamiento de enfermedades cardíacas de alta calidad. Una de las mejores herramientas de diagnóstico para caracterizar el corazón ha sido la imagen por resonancia magnética (IRM) gracias a sus capacidades de alta resolución tanto en la dimensión espacial como temporal, lo que permite generar imágenes dinámicas del corazón para un diagnóstico preciso. Las dimensiones del ventrículo izquierdo y la fracción de eyección derivada de ellos son los predictores más potentes de morbilidad y mortalidad cardiaca y su cuantificación tiene connotaciones importantes para el manejo y tratamiento de los pacientes. De esta forma, la IRM cardiaca es la técnica de imagen más exacta para la valoración del ventrículo izquierdo. Para obtener un diagnóstico preciso y rápido, se necesita un cálculo fiable de biomarcadores basados en imágenes a través de software de procesamiento de imágenes. Hoy en día la mayoría de las herramientas empleadas se basan en sistemas semiautomáticos de Diagnóstico Asistido por Computador (CAD) que requieren que el experto clínico interactúe con él, consumiendo un tiempo valioso de los profesionales cuyo objetivo debería ser únicamente interpretar los resultados. Un cambio de paradigma está comenzando a entrar en el sector médico donde los sistemas CAD completamente automáticos no requieren ningún tipo de interacción con el usuario. Estos sistemas están diseñados para calcular los biomarcadores necesarios para un diagnóstico correcto sin afectar el flujo de trabajo natural del médico y pueden iniciar sus cálculos en el momento en que se guarda una imagen en el sistema de archivo informático del hospital. Los sistemas CAD automáticos, aunque se consideran uno de los grandes avances en el mundo de la radiología, son extremadamente difíciles de desarrollar y dependen de tecnologías basadas en inteligencia artificial (IA) para alcanzar estándares médicos. En este contexto, el aprendizaje profundo (DL) ha surgido en la última década como la tecnología más exitosa para abordar este problema. Más específicamente, las redes neuronales convolucionales (CNN) han sido una de las técnicas más exitosas y estudiadas para el análisis de imágenes, incluidas las imágenes médicas. En este trabajo describimos las principales aplicaciones de CNN para sistemas CAD completamente automáticos para ayudar en la rutina de diagnóstico clínico mediante resonancia magnética cardíaca. El trabajo cubre los puntos principales a tener en cuenta para desarrollar tales sistemas y presenta diferentes resultados de alto impacto dentro del uso de CNN para resonancia magnética cardíaca, separados en tres proyectos diferentes que cubren su aplicación en la rutina clínica de diagnóstico, cubriendo los problemas de la segmentación, estimación automática de biomarcadores con explicabilidad y la detección de eventos. El trabajo completo presentado describe enfoques novedosos y de alto impacto para aplicar CNN al análisis de resonancia magnética cardíaca. El trabajo proporciona varios hallazgos clave, permitiendo varias formas de integración de esta reciente y creciente tecnología en sistemas CAD completamente automáticos que pueden producir resultados altamente precisos, rápidos y confiables. Los resultados descritos mejorarán e impactarán positivamente el flujo de trabajo de los expertos clínicos en un futuro próximo. / [CA] Les malalties cardiovasculars són una de les causes de mort i comorbiditat més predominants als països desenvolupats, s'han fet grans inversions en les últimes dècades per tal de produir eines de diagnòstic d'alta qualitat i aplicacions de tractament de malalties cardíaques. Una de les tècniques millor provades per caracteritzar el cor ha estat la imatge per ressonància magnètica (IRM), gràcies a les seves capacitats d'alta resolució tant en dimensions espacials com temporals, que permeten generar imatges dinàmiques del cor per a un diagnòstic precís. Les dimensions del ventricle esquerre i la fracció d'ejecció que se'n deriva són els predictors més potents de morbiditat i mortalitat cardíaca i la seva quantificació té connotacions importants per al maneig i tractament dels pacients. D'aquesta manera, la IRM cardíaca és la tècnica d'imatge més exacta per a la valoració del ventricle esquerre. Per obtenir un diagnòstic precís i ràpid, es necessita un càlcul fiable de biomarcadors basat en imatges mitjançant un programa de processament d'imatges. Actualment, la majoria de les ferramentes emprades es basen en sistemes semiautomàtics de Diagnòstic Assistit per ordinador (CAD) que requereixen que l'expert clínic interaccioni amb ell, consumint un temps valuós dels professionals, l'objectiu dels quals només hauria de ser la interpretació dels resultats. S'està començant a introduir un canvi de paradigma al sector mèdic on els sistemes CAD totalment automàtics no requereixen cap tipus d'interacció amb l'usuari. Aquests sistemes estan dissenyats per calcular els biomarcadors necessaris per a un diagnòstic correcte sense afectar el flux de treball natural del metge i poden iniciar els seus càlculs en el moment en què es deixa la imatge dins del sistema d'arxius hospitalari. Els sistemes CAD automàtics, tot i ser molt considerats com un dels propers grans avanços en el món de la radiologia, són extremadament difícils de desenvolupar i depenen de les tecnologies d'Intel·ligència Artificial (IA) per assolir els estàndards mèdics. En aquest context, l'aprenentatge profund (DL) ha sorgit durant l'última dècada com la tecnologia amb més èxit per abordar aquest problema. Més concretament, les xarxes neuronals convolucionals (CNN) han estat una de les tècniques més utilitzades i estudiades per a l'anàlisi d'imatges, inclosa la imatge mèdica. En aquest treball es descriuen les principals aplicacions de CNN per a sistemes CAD totalment automàtics per ajudar en la rutina de diagnòstic clínic mitjançant ressonància magnètica cardíaca. El treball recull els principals punts a tenir en compte per desenvolupar aquest tipus de sistemes i presenta diferents resultats d'impacte en l'ús de CNN a la ressonància magnètica cardíaca, tots separats en tres projectes principals diferents, cobrint els problemes de la segmentació, estimació automàtica de *biomarcadores amb *explicabilidad i la detecció d'esdeveniments. El treball complet presentat descriu enfocaments nous i potents per aplicar CNN a l'anàlisi de ressonància magnètica cardíaca. El treball proporciona diversos descobriments clau, que permeten la integració de diverses maneres d'aquesta tecnologia nova però en constant creixement en sistemes CAD totalment automàtics que podrien produir resultats altament precisos, ràpids i fiables. Els resultats descrits milloraran i afectaran considerablement el flux de treball dels experts clínics en un futur proper. / [EN] Cardiovascular diseases are one of the most predominant causes of death and comorbidity in developed countries, as such heavy investments have been done in recent decades in order to produce high quality diagnosis tools and treatment applications for cardiac diseases. One of the best proven tools to characterize the heart has been magnetic resonance imaging (MRI), thanks to its high-resolution capabilities in both spatial and temporal dimensions, allowing to generate dynamic imaging of the heart that enable accurate diagnosis. The dimensions of the left ventricle and the ejection fraction derived from them are the most powerful predictors of cardiac morbidity and mortality, and their quantification has important connotations for the management and treatment of patients. Thus, cardiac MRI is the most accurate imaging technique for left ventricular assessment. In order to get an accurate and fast diagnosis, reliable image-based biomarker computation through image processing software is needed. Nowadays most of the employed tools rely in semi-automatic Computer-Aided Diagnosis (CAD) systems that require the clinical expert to interact with it, consuming valuable time from the professionals whose aim should only be at interpreting results. A paradigm shift is starting to get into the medical sector where fully automatic CAD systems do not require any kind of user interaction. These systems are designed to compute any required biomarkers for a correct diagnosis without impacting the physician natural workflow and can start their computations the moment an image is saved within a hospital archive system. Automatic CAD systems, although being highly regarded as one of next big advances in the radiology world, are extremely difficult to develop and rely on Artificial Intelligence (AI) technologies in order to reach medical standards. In this context, Deep learning (DL) has emerged in the past decade as the most successful technology to address this problem. More specifically, convolutional neural networks (CNN) have been one of the most successful and studied techniques for image analysis, including medical imaging. In this work we describe the main applications of CNN for fully automatic CAD systems to help in the clinical diagnostics routine by means of cardiac MRI. The work covers the main points to take into account in order to develop such systems and presents different impactful results within the use of CNN to cardiac MRI, all separated in three different main projects covering the segmentation, automatic biomarker estimation with explainability and event detection problems. The full work presented describes novel and powerful approaches to apply CNN to cardiac MRI analysis. The work provides several key findings, enabling the integration in several ways of this novel but non-stop growing technology into fully automatic CAD systems that could produce highly accurate, fast and reliable results. The results described will greatly improve and impact the workflow of the clinical experts in the near future. / Pérez Pelegrí, M. (2023). Applications of Deep Leaning on Cardiac MRI: Design Approaches for a Computer Aided Diagnosis [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/192988

Diagnostic Captioning is described as “the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination” [59] and it can assist inexperienced doctors and radiologists to reduce clinical errors or help experienced professionals increase their productivity. In this context, tools that would help medical doctors produce higher quality reports in less time could be of high interest for medical imaging departments, as well as significantly impact deep learning research within the biomedical domain, which makes it particularly interesting for people involved in industry and researchers all along. In this work, we attempted to develop Diagnostic Captioning systems, based on novel Deep Learning approaches, to investigate to what extent Neural Networks are capable of performing medical image tagging, as well as automatically generating a diagnostic text from a set of medical images. Towards this objective, the first step is concept detection, which boils down to predicting the relevant tags for X-RAY images, whereas the ultimate goal is caption generation. To this end, we further participated in ImageCLEFmedical 2022 evaluation campaign, addressing both the concept detection and the caption prediction tasks by developing baselines based on Deep Neural Networks; including image encoders, classifiers and text generators; in order to get a quantitative measure of my proposed architectures’ performance [28]. My contribution to the evaluation campaign, as part of this work and on behalf of NeuralDynamicsLab¹ group at KTH Royal Institute of Technology, within the school of Electrical Engineering and Computer Science, ranked 4th in the former and 5th in the latter task [55, 68] among 12 groups included within the top-10 best performing submissions in both tasks. / Diagnostisk textning avser automatisk generering från en diagnostisk text från en uppsättning medicinska bilder av en patient som samlats in under en undersökning och den kan hjälpa oerfarna läkare och radiologer, minska kliniska fel eller hjälpa erfarna yrkesmän att producera diagnostiska rapporter snabbare [59]. Därför kan verktyg som skulle hjälpa läkare och radiologer att producera rapporter av högre kvalitet på kortare tid vara av stort intresse för medicinska bildbehandlingsavdelningar, såväl som leda till inverkan på forskning om djupinlärning, vilket gör den domänen särskilt intressant för personer som är involverade i den biomedicinska industrin och djupinlärningsforskare. I detta arbete var mitt huvudmål att utveckla system för diagnostisk textning, med hjälp av nya tillvägagångssätt som används inom djupinlärning, för att undersöka i vilken utsträckning automatisk generering av en diagnostisk text från en uppsättning medi-cinska bilder är möjlig. Mot detta mål är det första steget konceptdetektering som går ut på att förutsäga relevanta taggar för röntgenbilder, medan slutmålet är bildtextgenerering. Jag deltog i ImageCLEF Medical 2022-utvärderingskampanjen, där jag deltog med att ta itu med både konceptdetektering och bildtextförutsägelse för att få ett kvantitativt mått på prestandan för mina föreslagna arkitekturer [28]. Mitt bidrag, där jag representerade forskargruppen NeuralDynamicsLab² , där jag arbetade som ledande forskningsingenjör, placerade sig på 4:e plats i den förra och 5:e i den senare uppgiften [55, 68] bland 12 grupper som ingår bland de 10 bästa bidragen i båda uppgifterna.