<p>Quick and accurate segmentation and object detection of the biomedical image is the starting point of most disease analysis and understanding of biological processes in medical research. It will enhance drug development and advance medical treatment, especially in cancer-related diseases. However, identifying the objects in the CT or MRI images and labeling them usually takes time even for an experienced person. Currently, there is no automatic detection technique for nucleus identification, pneumonia detection, and fetus brain segmentation. Fortunately, as the successful application of artificial intelligence (AI) in image processing, many challenging tasks are easily solved with deep convolutional neural networks. In light of this, in this thesis, the deep learning based object detection and segmentation methods were implemented to perform the nucleus segmentation, lung segmentation, pneumonia detection, and fetus brain segmentation. The semantic segmentation is achieved by the customized U-Net model, and the instance localization is achieved by Faster R-CNN. The reason we choose U-Net is that such a network can be trained end-to-end, which means the architecture of this network is very simple, straightforward and fast to train. Besides, for this project, the availability of the dataset is limited, which makes U-Net a more suitable choice. We also implemented the Faster R-CNN to achieve the object localization. Finally, we evaluated the performance of the two models and further compared the pros and cons of them. The preliminary results show that deep learning based technique outperforms all existing traditional segmentation algorithms. </p>

Reducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient compared to the normal dose scans. Initially, we implemented wavelet denoising to successfully reduce the noise in low-dose X-ray computed tomography (CT) images. The denoising was improved by finding the optimal threshold value instead of a non-optimal selected value. The mean structural similarity (MSSIM) index was used as the objective function for the optimization. The denoising performance of combinations of wavelet families, wavelet orders, decomposition levels, and thresholding methods were investigated. Results of this study have revealed the best combinations of wavelet orders and decomposition levels for low dose CT denoising. In addition, a new shrinkage function is proposed that provides better denoising results compared to the traditional ones without requiring a selected parameter. Alternatively, convolutional neural networks were employed using different architectures to resolve the same denoising problem. This new approach improved denoising even more in comparison to the wavelet denoising. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Smartphones and mobile tablets are rapidly growing, and very important nowadays. The most popular mobile operating system since 2012 has been Android. Android is an open source platform that allows developers to take full advantage of both the operating system and the applications itself. However, due to the open source community of an Android platform, some Android developers took advantage of this and created countless malicious applications such as Trojan, Malware, and Ransomware. All which are currently hidden in a large number of benign apps in official Android markets, such as Google PlayStore, and Amazon. Ransomware is a malware that once infected the victim’s device. It will encrypt files, unlock device system, and display a popup message which asks the victim to pay ransom in order to unlock their device or system which may include medical devices that connect through the internet. In this research, we propose to combine permission and API calls, then use Deep Learning techniques to detect ransomware apps from the Android market. Permissions setting and API calls are extracted from each app file by using a python library called AndroGuard. We are using Permissions and API call features to characterize each application, which can identify which application has potential to be ransomware or is benign. We implement our Android Ransomware Detection framework based on Keras, which uses MLP with back-propagation and a supervised algorithm. We used our method with experiments based on real-world applications with over 2000 benign applications and 1000 ransomware applications. The dataset came from ARGUS’s lab [1] which validated algorithm performance and selected the best architecture for the multi-layer perceptron (MLP) by trained our dataset with 6 various of MLP structures. Our experiments and validations show that the MLPs have over 3 hidden layers with medium sized of neurons achieved good results on both accuracy and AUC score of 98%. The worst score is approximately 45% to 60% and are from MLPs that have 2 hidden layers with large number of neurons. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want to retrieve important moments such as the “first steps of her child” from a large collection of untrimmed videos; or retrieving all the instances of a home-run from an unsegmented video of baseball. In the face of this data deluge, it becomes crucial to develop efficient and scalable algorithms that can intelligently localize semantic visual content in untrimmed videos. In this work, I address three different challenges on the localization of actions in videos. First, I develop deep-based action proposals and detection models that take a video and generate action-agnostic and class-specific temporal segments, respectively. These models retrieve temporal locations with high accuracy in an efficient manner, faster than real-time. Second, I propose the new task to retrieve and localize temporal moments from a collection of videos given a natural language query. To tackle this challenge, I introduce an efficient and effective model that aligns the text query to individual clips of fixed length while still retrieves moments spanning multiple clips. This approach not only allows smooth interactions with users via natural languagequeries but also reduce the index size and search time for retrieving the moments. Lastly, I introduce the concept of actor-supervision that exploits the inherent compo sitionality of actions, in terms of transformations of actors, to achieve spatiotemporal localization of actions without the need of action box annotations. By designing ef ficient models to scan a single video in real-time; retrieve and localizing moments of interest from multiple videos; and an effective strategy to localize actions without resorting in action box annotations, this thesis provides insights that put us closer to the goal of general video understanding.

Manual analysis of medical images such as magnetic resonance imaging (MRI) requires a trained professional, is time-consuming and results may vary between experts. We propose an automated method for kidney segmentation using a convolutional Neural Network (CNN) model based on the U-Net architecture. Investigations are done to compare segmentations between trained experts, inexperienced operators and the Neural Network model, showing near human expert level performance from the Neural Network. Stratified sampling is performed when selecting which subject volumes to perform manual segmentations on to create training data. Experiments are run to test the effectiveness of transfer learning and data augmentation and we show that one of the most important components of a successful machine learning pipeline is larger quantities of carefully annotated data for training.

Submitted by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-06-30T17:22:52Z No. of bitstreams: 1 DIS_ANIBAL_SOLON_HEINSFELD_COMPLETO.pdf: 12807619 bytes, checksum: d11b60094a8bde0d839a6f7a23bbb56c (MD5) / Made available in DSpace on 2017-06-30T17:22:52Z (GMT). No. of bitstreams: 1 DIS_ANIBAL_SOLON_HEINSFELD_COMPLETO.pdf: 12807619 bytes, checksum: d11b60094a8bde0d839a6f7a23bbb56c (MD5) Previous issue date: 2016-03-28 / O Espectro Autista (EA) compreende uma s?rie de desordens no desenvolvimento neurol?gico, caracterizado por defici?ncias sociais e dificuldades de comunica??o, comportamentos repetitivos e atrasos cognitivos. Atualmente, o diagn?stico do EA ? amplamente baseado em medi??es comportamentais, que pode ser demorado, e depende da coopera??o do paciente e da experi?ncia do examinador. Para mitigar esta limita??o, investigamos padr?es neurais que ajudem no diagn?stico de desordens do EA. Nesta disserta??o, usamos t?cnicas de deep learning, a fim de extrair caracter?sticas robustas de neuroimagens de pacientes com autismo. Neuroimagens cont?m cerca de 300.000 pontos espaciais, com aproximadamente 200 medi??es cada. As t?cnicas de deep learning s?o ?teis para extrair caracter?sticas relevantes que diferenciam autistas de n?o-autistas. Ao utilizar denoising autoencoders, uma t?cnica de deep learning espec?fica que visa reduzir a dimensionalidade dos dados, n?s superamos o estado da arte, atingindo 69% de acur?cia, comparado com o melhor resultado encontrado na literatura, com 60% de acur?cia. / Autism Spectrum Disorders (ASD) comprise a range of neurodevelopmental disorders, characterized by social deficits and communication difficulties, repetitive behaviors, and cognitive delays. The diagnosis of ASD is largely based on behavioral measurements, which can be timeconsuming and relies on the patient cooperation and examiner expertise. In order to address this limitation, we aim to investigate neural patterns to help in the diagnosis of ASD. In this dissertation, we use deep learning techniques to extract robust characteristics from neuroimages of autistic subject brain function. Since neuroimage contains about 300,000 spatial points, with approximately 200 temporal measurements each, deep learning techniques are useful in order to extract important features to discriminate ASD subjects from non-ASD. By using denoising autoencoders, a specific deep learning technique that aims to reduce data dimensionality, we surpass the state-of-the-art by achieving 69% of accuracy, compared to 60% using the same dataset.

This work presents my research about the use of Convolutional Neural Network (CNNs) for transfer learning through its application for colonic polyp classification and iris super-resolution. Traditionally, machine learning methods use the same feature space and the same distribution for training and testing the tools. Several problems in this approach can emerge as, for example, when the number of samples for training (especially in a supervised training) is limited. In the medical field, this problem is recurrent mainly because obtaining a database large enough with appropriate annotations for training is highly costly and may become impractical. Another problem relates to the distribution of textural features in a image database which may be too large such as the texture patterns of the human iris. In this case a single and specific training database might not get enough generalization to be applied to the entire domain. In this work we explore the use of texture transfer learning to surpass these problems for two applications: colonic polyp classification and iris super-resolution. The leading cause of deaths related to intestinal tract is the development of cancer cells (polyps) in its many parts. An early detection (when the cancer is still at an early stage) can reduce the risk of mortality among these patients. More specifically, colonic polyps (benign tumors or growths which arise on the inner colon surface) have a high occurrence and are known to be precursors of colon cancer development. Several studies have shown that automatic detection and classification of image regions which may contain polyps within the colon can be used to assist specialists in order to decrease the polyp miss rate. However, the classification can be a difficult task due to several factors such as the lack or excess of illumination, the blurring due to movement or water injection and the different appearances of polyps. Also, to find a robust and a global feature extractor that summarizes and represents all these pit-patterns structures in a single vector is very difficult and Deep Learning can be a good alternative to surpass these problems. One of the goals of this work is show the effectiveness of CNNs trained from scratch for colonic polyp classification besides the capability of knowledge transfer between natural images and medical images using off-the-shelf pretrained CNNs for colonic polyp classification. In this case, the CNN will project the target database samples into a vector space where the classes are more likely to be separable. The second part of this work dedicates to the transfer learning for iris super-resolution. The main goal of Super-Resolution (SR) is to produce, from one or more images, an image with a higher resolution (with more pixels) at the same time that produces a more detailed and realistic image being faithful to the low resolution image(s). Currently, most iris recognition systems require the user to present their iris for the sensor at a close distance. However, at present, there is a constant pressure to make that relaxed conditions of acquisitions in such systems could be allowed. In this work we show that the use of deep learning and transfer learning for single image super resolution applied to iris recognition can be an alternative for Iris Recognition of low resolution images. For this purpose, we explore if the nature of the images as well as if the pattern from the iris can influence the CNN transfer learning and, consequently, the results in the recognition process. / Diese Arbeit pr¨asentiert meine Forschung hinsichtlich der Verwendung von ”Transfer-Learning” (TL) in Kombination mit Convolutional Neural Networks (CNNs), um dadurch die Klassifikation von Dickdarmpolypen und die Qualit¨at von Iris Bildern (”Iris-Super-Resolution”) zu verbessern. Herk¨ommlicherweise verwenden Verfahren des maschinellen Lernens den gleichen Merkmalsraum und die gleiche Verteilung zum Trainieren und Testen der abgewendeten Methoden. Mehrere Probleme k¨onnen bei diesem Ansatz jedoch auftreten. Zum Beispiel ist es m¨ oglich, dass die Anzahl der zu trainierenden Daten (insbesondere in einem ”supervised training” Szenario) begrenzt ist. Im Speziellen im medizinischen Anwendungsfall ist man regelm¨aßig mit dem angesprochenen Problem konfrontiert, da die Zusammenstellung einer Datenbank, welche ¨ uber eine geeignete Anzahl an verwendbaren Daten verf ¨ ugt, entweder sehr kostspielig ist und/oder sich als ¨ uber die Maßen zeitaufw¨andig herausstellt. Ein anderes Problem betrifft die Verteilung von Strukturmerkmalen in einer Bilddatenbank, die zu groß sein kann, wie es im Fall der Verwendung von Texturmustern der menschlichen Iris auftritt. Dies kann zu dem Umstand f ¨ uhren, dass eine einzelne und sehr spezifische Trainingsdatenbank m¨oglicherweise nicht ausreichend verallgemeinert wird, um sie auf die gesamte betrachtete Dom¨ane anzuwenden. In dieser Arbeit wird die Verwendung von TL auf diverse Texturen untersucht, um die zuvor angesprochenen Probleme f ¨ ur zwei Anwendungen zu ¨ uberwinden: in der Klassifikation von Dickdarmpolypen und in Iris Super-Resolution. Die Hauptursache f ¨ ur Todesf¨alle im Zusammenhang mit dem Darmtrakt ist die Entwicklung von Krebszellen (Polypen) in vielen unterschiedlichen Auspr¨agungen. Eine Fr ¨uherkennung kann das Mortalit¨atsrisiko bei Patienten verringern, wenn sich der Krebs noch in einem fr ¨uhen Stadium befindet. Genauer gesagt, Dickdarmpolypen (gutartige Tumore oder Wucherungen, die an der inneren Dickdarmoberfl¨ache entstehen) haben ein hohes Vorkommen und sind bekanntermaßen Vorl¨aufer von Darmkrebsentwicklung. Mehrere Studien haben gezeigt, dass die automatische Erkennung und Klassifizierung von Bildregionen, die Polypen innerhalb des Dickdarms m¨oglicherweise enthalten, verwendet werden k¨onnen, um Spezialisten zu helfen, die Fehlerrate bei Polypen zu verringern. Die Klassifizierung kann sich jedoch aufgrund mehrerer Faktoren als eine schwierige Aufgabe herausstellen. ZumBeispiel kann das Fehlen oder ein U¨ bermaß an Beleuchtung zu starken Problemen hinsichtlich der Kontrastinformation der Bilder f ¨ uhren, wohingegen Unsch¨arfe aufgrund von Bewegung/Wassereinspritzung die Qualit¨at des Bildmaterials ebenfalls verschlechtert. Daten, welche ein unterschiedlich starkes Auftreten von Polypen repr¨asentieren, bieten auch dieM¨oglichkeit zu einer Reduktion der Klassifizierungsgenauigkeit. Weiters ist es sehr schwierig, einen robusten und vor allem globalen Feature-Extraktor zu finden, der all die notwendigen Pit-Pattern-Strukturen in einem einzigen Vektor zusammenfasst und darstellt. Um mit diesen Problemen ad¨aquat umzugehen, kann die Anwendung von CNNs eine gute Alternative bieten. Eines der Ziele dieser Arbeit ist es, die Wirksamkeit von CNNs, die von Grund auf f ¨ ur die Klassifikation von Dickdarmpolypen konstruiert wurden, zu zeigen. Des Weiteren soll die Anwendung von TL unter der Verwendung vorgefertigter CNNs f ¨ ur die Klassifikation von Dickdarmpolypen untersucht werden. Hierbei wird zus¨atzliche Information von nichtmedizinischen Bildern hinzugezogen und mit den verwendeten medizinischen Daten verbunden: Information wird also transferiert - TL entsteht. Auch in diesem Fall projiziert das CNN iii die Zieldatenbank (die Polypenbilder) in einen vorher trainierten Vektorraum, in dem die zu separierenden Klassen dann eher trennbar sind, daWissen aus den nicht-medizinischen Bildern einfließt. Der zweite Teil dieser Arbeit widmet sich dem TL hinsichtlich der Verbesserung der Bildqualit¨at von Iris Bilder - ”Iris- Super-Resolution”. Das Hauptziel von Super-Resolution (SR) ist es, aus einem oder mehreren Bildern gleichzeitig ein Bild mit einer h¨oheren Aufl¨osung (mit mehr Pixeln) zu erzeugen, welches dadurch zu einem detaillierteren und somit realistischeren Bild wird, wobei der visuelle Bildinhalt unver¨andert bleibt. Gegenw¨artig fordern die meisten Iris- Erkennungssysteme, dass der Benutzer seine Iris f ¨ ur den Sensor in geringer Entfernung pr¨asentiert. Jedoch ist es ein Anliegen der Industrie die bisher notwendigen Bedingungen - kurzer Abstand zwischen Sensor und Iris, sowie Verwendung von sehr teuren hochqualitativen Sensoren - zu ver¨andern. Diese Ver¨anderung betrifft einerseits die Verwendung von billigeren Sensoren und andererseits die Vergr¨oßerung des Abstandes zwischen Iris und Sensor. Beide Anpassungen f ¨ uhren zu Reduktion der Bildqualit¨at, was sich direkt auf die Erkennungsgenauigkeit der aktuell verwendeten Iris- erkennungssysteme auswirkt. In dieser Arbeit zeigen wir, dass die Verwendung von CNNs und TL f ¨ ur die ”Single Image Super-Resolution”, die bei der Iriserkennung angewendet wird, eine Alternative f ¨ ur die Iriserkennung von Bildern mit niedriger Aufl¨osung sein kann. Zu diesem Zweck untersuchen wir, ob die Art der Bilder sowie das Muster der Iris das CNN-TL beeinflusst und folglich die Ergebnisse im Erkennungsprozess ver¨andern kann.

The contact patterns among individuals can significantly affect the progress of an infectious outbreak within a population. Gathering data about these interaction and mixing patterns is essential to assess computational modeling of infectious diseases. Various self-report approaches have been designed in different studies to collect data about contact rates and patterns. Recent advances in sensing technology provide researchers with a bilateral automated data collection devices to facilitate contact gathering overcoming the disadvantages of previous approaches. In this study, a novel unilateral wearable sensing architecture has been proposed that overcome the limitations of the bi-lateral sensing. Our unilateral wearable sensing system gather contact data using hybrid sensor arrays embedded in wearable shirt. A smartphone application has been used to transfer the collected sensors data to the cloud and apply deep learning model to estimate the number of human contacts and the results are stored in the cloud database. The deep learning model has been developed on the hand labelled data over multiple experiments. This model has been tested and evaluated, and these results were reported in the study. Sensitivity analysis has been performed to choose the most suitable image resolution and format for the model to estimate contacts and to analyze the model's consumption of computer resources.

Cardiovascular diseases are the largest mortality factor globally, and early diagnosis is essential for a proper medical response. Cardiac computed tomography can be used to acquire images for their diagnosis, but without radiation dose reduction the radiation emitted to the patient becomes a significant risk factor. By reducing the dose, the image quality is often compromised, and determining a diagnosis becomes difficult. This project proposes image quality enhancement with deep learning. A cycle-consistent generative adversarial neural network was fed low- and high-quality images with the purpose to learn to translate between them. By using a cycle-consistency cost it was possible to train the network without paired data. With this method, a low-quality image acquired from a computed tomography scan with dose reduction could be enhanced in post processing. The results were mixed but showed an increase of ventricular contrast and artifact mitigation. The technique comes with several problems that are yet to be solved, such as structure alterations, but it shows promise for continued development.

This thesis presents novel tasks and deep learning methods for machine reading comprehension and question answering with the goal of achieving natural language understanding. First, we consider a semantic parsing task where the model understands sentences and translates them into a logical form or instructions. We present a novel semi-supervised sequential autoencoder that considers language as a discrete sequential latent variable and semantic parses as the observations. This model allows us to leverage synthetically generated unpaired logical forms, and thereby alleviate the lack of supervised training data. We show the semi-supervised model outperforms a supervised model when trained with the additional generated data. Second, reading comprehension requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess reading comprehension ability, in both artificial agents and children learning to read. We propose a new, challenging, supervised reading comprehension task. We gather a large-scale dataset of news stories from the CNN and Daily Mail websites with Cloze-style questions created from the highlights. This dataset allows for the first time training deep learning models for reading comprehension. We also introduce novel attention-based models for this task and present qualitative analysis of the attention mechanism. Finally, following the recent advances in reading comprehension in both models and task design, we further propose a new task for understanding complex narratives, NarrativeQA, consisting of full texts of books and movie scripts. We collect human written questions and answers based on high-level plot summaries. This task is designed to encourage development of models for language understanding; it is designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard reading comprehension models struggle on the tasks presented here.