Software Engineering Department Master Thesis

Son zamanlarda yapay zeka (AI), sunduğu çözümler nedeniyle bilimsel araştırmaların tüm alanlarını işgal etti. Sağlık da bir istisna değil. Diyabet dünyadaki en yaygın hastalıklardan biridir. Komplikasyonlarından biri, hastanın görüşünü bulanıklaştırabilen veya bozabilen ve körlüğün ana nedenlerinden biri olan diyabetik retinopatidir. Diyabetik retinopatinin erken teşhisi tedaviye büyük ölçüde yardımcı olabilir. Yapay Zeka ve özellikle derin öğrenme alanındaki son gelişmeler, birçok hastalığı erken evrelerinde tahmin etmek, öngörmek ve teşhis etmek için kullanılabilecek iddialı çözümler sunmaktadır. Son yıl projemizde, retina görüntülerini analiz etmek için derin öğrenmenin potansiyelini araştırdık. Diyabetik retinopati seviyelerini otomatik olarak tespit etmemizi ve sınıflandırmamızı sağlayacak bir model oluşturmak için Derin Öğrenme (DL) kavramlarını bir konvolüsyonel sinir ağı (CNN) algoritması ile inceleyeceğiz. Göz ve diyabetik retinopati, ardından farklı diyabetik retinopati türleri, diyabetik retinopatinin nedenleri, önlenmesi, teşhisi ve uygun tedavisi hakkında bir sunum yapacağız. Modellerimizi eğitmek için herkesin erişebileceği bir platform olan Google Colab'ı kullanacağız

A Review on Machine Learning Methods in Diabetic Retinopathy Detection

Ocular disorders have a broad spectrum. Some of them, such as Diabetic Retinopathy, are more common in low-income or low-resource countries. Diabetic Retinopathy is a cause related to vision loss and ocular impairment in the world. By identifying the symptoms in the early stages, it is possible to prevent the progress of the disease and also reach blindness. Considering the prevalence of different branches of Artificial Intelligence in many fields, including medicine, and the significant progress achieved in the use of big data to investigate ocular impairments, the potential of Artificial Intelligence algorithms to process and analyze Fundus images was used to identify symptoms associated with Diabetic Retinopathy. Under the studies, the proposed models for transformers provide better interpretability for doctors and scientists. Artificial Intelligence algorithms are also helpful in anticipating future health issues after appraising premature cases of the ailment. Especially in ophthalmology, a trustworthy diagnosis of visual outcomes helps physicians in advising disease and clinical decision-making while reducing health management costs

Development of an automated screening tool for diabetic retinopathy using artificial intelligence

Diabetic retinopathy is the commonest cause of blindness in the working age population in the Western world. It is widely recognised that screening for this treatable condition is highly cost effective. However, there is a shortage in the number of trained personnel required to screen for sight threatening forms of the disease. It has been shown that many of the features of diabetic retinopathy such as microaneurysms, cotton wool spots, exudates and haemorrhages can be identified automatically with high levels of sensitivity and specificity. This work describes the development of an automated computerised system for the screening of diabetic retinopathy through the integration of an artificial intelligent system and the development of custom written software (Diabetic Retinopathy Image Classification Programme) to enable image acquisition, image processing, neural network training and testing to be performed in a structured manner. A combination of conventional image processing and neural network methods are utilised for the identification of the basic features associated with the normal and diabetic fundus image. Preliminary investigations into the identification of sight-threatening features are also described. Identification of normal retinal vasculature and diabetic associated features was performed using three separately trained back-propagtion neural networks. Localisation of the optic disc and macula was achieved by region of interest pixel intensity scanning. Assessment of the optic disc for sight-threatening new vessel growth was performed by comparing the variance in circular intensity profiles of normal optic discs to the variance of those with neovascularisation. Patients were classified as having maculopathy if hard exudates were identified within one disc diameter of the fovea. The overall aim of this project is to develop an automated screening programme for diabetic retinopathy. The initial phase details the development and comparison of a range of algorithms for the detection of features associated with diabetic retinopathy. The final phase details the clinical evaluation of the current screening system

Automated classification of retinopathy of prematurity in newborns

La Retinopatia de l'Prematur (ROP) és una malaltia que afecta els nadons prematurs mostrant-se com un subdesenvolupament dels vasos retinians. El diagnòstic precoç d'aquesta malaltia és un tot un repte ja que requereix de professionals altament qualificats amb coneixements molt específics. Actualment a Espanya, només uns pocs hospitals compten amb els equipaments especialitzats per al tractament i diagnòstic d'aquesta patologia. Aquest projecte final de màster, té com a objectiu final desenvolupar una eina preliminar per a la classificació de l'extensió aquesta malaltia. Aquesta applicació, ha estat disenyada per a ser integrada en una plataforma de suport a la diagnosi de la Retinopatia i poder evaluar la malaltia, proporcionant informació detallada sobre les imatge analitzades. Aquest projecte, també estableix les bases per a la comparació entre l'enfocament clínic, que utilitzen els metges, i la naturalesa "Black-Box" natural de la Xarxa Neuronal Artificial per classificar l'extensió de la malaltia. L'algoritme desenvolupat és capaç de: segmentar els vasos oculars utilitzant una xarxa neuronal convolucional U-Net; extreure les característiques representatives de la malaltia a partir de la segmentació; i classificar aquestes característiques en casos ROP i casos ROP Plus, mitjançant l'ús d'una gamma de classificadors. Les principals característiques analitzades són la tortuositat i el gruix dels vasos, indicadors de la malaltia emprats pels patolegs experts. La xarxa de segmentació ha obtingut una precisió global de l'96,15%. Els resultats dels diferents classificadors indiquen un trade-off entre la precisió i el volum d'imatges analitzades. S'ha obtingut una precisió de l'100% emprant un classificador de doble threshold en el analisis de l'12,5% de les imatges. En canvi, mitjançant l'ús d'un classificador "decision tree", s'ha obtingut una precisió del 70,8% analitzant el 100% de les imatges.La Retinopatía del Prematuro (ROP) es una enfermedad que afecta a los bebés prematuros mostrándose como el subdesarrollo de los vasos retinianos. El diagnóstico precoz de dicha enfermedad es un desafío ya que requiere de profesionales altamente capacitados con conocimientos muy específicos. Actualmente en España, solo unos pocos hospitales están dotados con los equipamientos especializados para el tratamiento y diagnóstico de esta patología Este proyecto final de master, tiene como objetivo final desarrollar una herramienta preliminar para la clasificación de la extensión dicha enfermedad. Esta aplicación, ha sido diseñada para ser integrada en una plataforma de soporte al diagnóstico de la Retinopatía y evaluar la enfermedad, proporcionando información detallada sobre las imágenes analizadas. Este proyecto también sienta las bases para la comparación entre el enfoque clínico, que utilizan los médicos, y la naturaleza "Black-Box" natural de la Red Neuronal Artificial para clasificar la extensión de la enfermedad. El algoritmo desarrollado es capaz de: segmentar los vasos oculares utilizando una red neuronal convolucional U-Net; extraer las características representativas de la enfermedad a partir de la segmentación; y clasificar estas características en casos ROP y casos ROP Plus, mediante el empleo de una gama de clasificadores. Las principales características analizadas son la tortuosidad y el grosor de los vasos, indicadores cauterizantes de la enfermedad empleados por los patólogos expertos. La red de segmentación ha logrado una precisión global del 96,15%. Los resultados de los diferentes clasificadores indican un trade-off entre la precisión y el volumen de imágenes analizadas. Se ha obtenido una precisión del 100% empleando un clasificador de doble threshold en el análisis del 12,5% de las imágenes. En cambio, mediante el uso de un clasificador “decision tree”, se ha obtenido una precisión del 70,8% analizando el 100% de las imágenes.Retinopathy of Prematurity (ROP) is a disease in preterm babies with underdevelopment in retinal vessels. Early diagnosis of the disease is challenging and requires skilled professionals with very specific knowledge. Currently, in Spain, only a few hospitals have departments specialized in this pathology and, therefore, are able to diagnose and treat it accordingly. This master project aims to develop the first preliminary instrument for the classification of the extent of Retinopathy disease. This tool has been built to be integrated into a diagnostic support platform to detect the presence of retinopathy and evaluate the sickness, providing insightful information regarding the specific image. This project also lays the base for the comparison between the clinical approach that the doctors use and the “black box” approach the Artificial Neural Network uses to predict the extent of the disease. The developed algorithm is able to: segment ocular vessels using a U-Net Convolutional Neural Network; extract the critical features from the segmentation; and classify those features into ROP cases and ROP Plus cases by employing a range of different classifiers. The main features analyzed by the related specialists and thus selected are tortuosity and thickness of the vessels. The segmentation Network achieved a global accuracy of 96.15%. The results of the different classifiers indicate a trade-off between accuracy and the volume of computed images. An accuracy of 100% was achieved with a Double Threshold classifier on 12.5% of the images. Instead, by using a Decision tree classifier, an accuracy of 70.8% was achieved when computing 100% of the images

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

Koneoppiminen päätöksenteon tukijana diabetes mellituksen hoidossa

Koneoppiminen on yksi tekoälyn osa-alue, jota voidaan hyödyntää laajasti terveydenhuollossa erilaisiin käyttötarkoituksiin. Diabetes hoidossa koneoppimisteknologioiden käyttöönotto voi merkitä huomattavaa laadullista parannusta ja kustannustehokasta hoitoa. Tutkimuksen tavoitteena on tuottaa käyttökelpoista ja ohjeellistavaa tietoa koneoppimisen soveltamismahdollisuuksista toimivan kliinisen ei-tietämyskantaisen päätöksenteon tukijärjestelmän suunnittelumallin luomiseksi terveydenhuolto-organisaatioihin, terveydenhuollon ammattihenkilökunnan kliinisen päätöksenteon edistämiseksi. Tutkimuksen teoreettisen viitekehyksen muodostaa koneoppiminen terveydenhoidossa ja kliininen päätöksenteko. Tutkimuksen osioita ovat koneoppimisen sovellettavuus diabeteshoitoon, koneoppimisen soveltaminen diabetes hoitotulosten ennustamiseen ja koneoppiminen diabeteksen diagnosointityökaluna. Tutkimusmenetelmä on kvalitatiivinen, integroiva kirjallisuuskatsaus. Aineisto kerättiin useasta eri tietokannasta, ja se muodostuu pääasiassa tieteellisistä katsaus-, tutkimus- ja konferenssiartikkeleista. Tutkimuksen aineisto analysoitiin ymmärtämään pyrkivällä laadullisella analyysilla. Tämä tehtiin induktiivisella lähestymistavalla aineistolähtöisenä sisällönanalyysina. Integroivan kirjallisuuskatsauksen synteesin pohjalta saatu tutkimustulos vastaa esitettyihin tutkimuskysymyksiin ja määrittelee toimivan ei-tietämyskantaisen kptj:n vaatimuksia järjestelmän varsinaista suunnittelua ja teknistä toteutusta varten. Tulokset osoittavat, että koneoppimistekniikoista syväoppiminen, ohjaamaton oppiminen, ohjattu oppiminen, yhteen liittynyt koneoppiminen ja äärimmäinen oppimiskone ovat niitä koneoppimisalgoritmeja, joita pitäisi integroida mukaan ei-tietoon-perustuvaan kliiniseen päätöksenteon tukijärjestelmään, varsinaisen kliinisen päätöksenteko prosessin tukemiseksi diabetes hoidossa. Tutkimuksen tuloksia on selostettu tarkemmin diskussio kappaleessa ja rajoitukset on myös pyritty tuomaan esille

Deep Machine Learning with Spatio-Temporal Inference

Deep Machine Learning (DML) refers to methods which utilize hierarchies of more than one or two layers of computational elements to achieve learning. DML may draw upon biomemetic models, or may be simply biologically-inspired. Regardless, these architectures seek to employ hierarchical processing as means of mimicking the ability of the human brain to process a myriad of sensory data and make meaningful decisions based on this data. In this dissertation we present a novel DML architecture which is biologically-inspired in that (1) all processing is performed hierarchically; (2) all processing units are identical; and (3) processing captures both spatial and temporal dependencies in the observations to organize and extract features suitable for supervised learning. We call this architecture Deep Spatio-Temporal Inference Network (DeSTIN). In this framework, patterns observed in pixel data at the lowest layer of the hierarchy are organized and fit to generalizations using decomposition algorithms. Subsequent spatial layers draw upon previous layers, their own temporal observations and beliefs, and the observations and beliefs of parent nodes to extract features suitable for supervised learning using standard classifiers such as feedforward neural networks. Hence, DeSTIN is viewed as an unsupervised feature extraction scheme in the sense that rather than relying on human engineering to determine features for a particular problem, DeSTIN naturally constructs features of interest by representing salient regularities in the patterns observed. Detailed discussion and analysis of the DeSTIN framework is provided, including focus on its key components of generalization through online clustering and temporal inference. We present a variety of implementation details, including static and dynamic learning formulations, and function approximation methods. Results on standardized datasets of handwritten digits as well as face and optic nerve detection are presented, illustrating the efficacy of the proposed approach