While generative adversarial networks (GAN) have been widely applied in various settings, the competitive deep learning frameworks such as GANs were not as popular in medical image processing and even less widely applied on high resolution data due to the issues related to their stability. In this dissertation, we examined optimal ways of modeling a generalizable competitive framework that can alleviate the inherent stability issues while still meeting additional objectives such as to achieve prediction accuracy of a classification task or to satisfy other performance metrics on high dimensional data sets. The first part of the thesis is focused on exploring better network performance in a non-competitive setting with a closed-form solution. (1) We introduced Pyramid Encoder in seq2seq models and observed a significant increase in computational and memory efficiency while achieving a similar repair rate to their non-pyramid counterparts. (2) We proposed a mixed spatio-temporal neural network for real-time prediction of crimes, establishing the feasibility of a convolutional neural network (CNN) in the spatio-temporal domain. (3) We developed and validated an interpretable deep learning framework for Alzheimer’s disease (AD) classification as a clinically adaptable strategy to generate neuroimaging signatures for AD diagnosis and as a generalizable approach for linking deep learning to pathophysiological processes in human disease. (4) We designed and validated an end-to-end survival model for prediction of progression from mild cognitive impairment (MCI) to AD, and identified regions salient to predicting progression from MCI to AD. (5) Additionally, we applied a supervised learning framework in Parrondo's Paradox that maps playing history directly to the decision space, and learned to combine two individually-losing games to have a positive expectation. The second part is focused on the design and analysis of neural models in a competitive setting without a closed-form solution. We extended the models from tackling a single objective to multiple tasks, while also moving from two-dimensional images to three-dimensional magnetic resonance imaging scans of the human brain. (1) We experimented with domain-specific inpainting with a concurrently pre-trained GAN to recover noised or cropped images. (2) We developed a GAN model to enhance MRI-driven AD classification performance using generative adversarial learning. (3) Finally, we proposed a competitive framework that could recover 3D medical data from 2D slices, while retaining disease-related information. / 2023-07-04T00:00:00Z

No / Typically characterised as a movement disorder, bradykinesia can be represented according to the degree of motor impairment. The assessment criteria for Parkinson’s disease (PD) is therefore well defined due to its symptomatic nature. Diagnosing and monitoring the progression of bradykinesia is currently heavily reliant on clinician’s visual judgment. One of the most common forms of examining bradykinesia involves rapid finger tapping and is aimed to determine the patient’s ability to initiate and sustain movement effectively. This consists of the patient repeatedly tapping their index finger and thumb together. Object detection algorithm, YOLO, was trained to track the separation between the index finger and thumb. Bounding boxes (BB) were used to determine their relative position on a frame-to-frame basis to produce a time series signal. Key movement characteristics were extracted to determine regularity of movement in finger tapping amongst Parkinson’s patients and controls.

Deep neural networks are powerful tools of machine learning. Despite their capabilities of fitting the training data, they tend to perform undesirably on the unseen data. To improve the generalization of the deep neural networks, a variety of regularization techniques have been proposed. This thesis studies a simple yet effective regularization scheme, Mixup, which has been proposed recently. Briefly speaking, Mixup creates synthetic examples by linearly interpolating random pairs of the real examples and uses the synthetic examples for training. Although Mixup has been empirically shown to be effective on various classification tasks for neural network models, its working mechanism and possible limitations have not been well understood. One potential problem of Mixup is known as manifold intrusion, in which the synthetic examples "intrude" the data manifolds of the real data, resulting in the conflicts between the synthetic labels and the ground-truth labels of the synthetic examples. The first part of this thesis investigates the strategies for resolving the manifold intrusion problem. We focus on two strategies. The first strategy, which we call "relabelling", attempts to find better labels for the synthetic data; the second strategy, which we call "cautious mixing", carefully selects the interpolating parameters to generate the synthetic examples. Through extensive experiments over several design choices, we observe that the "cautious mixing" strategy appears to perform better. The second part of this thesis reports a previously unobserved phenomenon in Mixup training: on a number of standard datasets, the performance of the Mixup-trained models starts to decay after training for a large number of epochs, giving rise to a U-shaped generalization curve. This behavior is further aggravated when the size of the original dataset is reduced. To help understand such a behavior of Mixup, we show theoretically that Mixup training may introduce undesired data-dependent label noises to the synthetic data. Via analyzing a least-square regression problem with a random feature model, we explain why noisy labels may cause the U-shaped curve to occur: Mixup improves generalization through fitting the clean patterns at the early training stage, but as training progresses, the model becomes over-fitting to the noise in the synthetic data. Extensive experiments are performed on a variety of benchmark datasets, validating this explanation.

Conventional methods for solving image restoration problems are typically built on an image degradation model and on some priors of the latent image. The model of the degraded image and the prior knowledge of the latent image are necessary because the restoration is an ill posted inverse problem. However, for some applications, such as those addressed in this thesis, the image degradation process is too complex to model precisely; in addition, mathematical priors, such as low rank and sparsity of the image signal, are often too idealistic for real world images. These difficulties limit the performance of existing image restoration algorithms, but they can be, to certain extent, overcome by the techniques of machine learning, particularly deep convolutional neural networks. Machine learning allows large sample statistics far beyond what is available in a single input image to be exploited. More importantly, the big data can be used to train deep neural networks to learn the complex non-linear mapping between the degraded and original images. This circumvents the difficulty of building an explicit realistic mathematical model when the degradation causes are complex and compounded. In this thesis, we design and implement deep convolutional neural networks (DCNN) for two challenging image restoration problems: reflection removal and joint demosaicking-deblurring. The first problem is one of blind source separation; its DCNN solution requires a large set of paired clean and mixed images for training. As these paired training images are very difficult, if not impossible, to acquire in the real world, we develop a novel technique to synthesize the required training images that satisfactorily approximate the real ones. For the joint demosaicking-deblurring problem, we propose a new multiscale DCNN architecture consisting of a cascade of subnetworks so that the underlying blind deconvolution task can be broken into smaller subproblems and solved more effectively and robustly. In both cases extensive experiments are carried out. Experimental results demonstrate clear advantages of the proposed DCNN methods over existing ones. / Thesis / Master of Applied Science (MASc)

Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis. In this paper, I propose an end-to-end generative method for single image dehazing problem. It is based on fully convolutional network and effective network structures to recognize haze structure in input images and restore clear, haze-free ones. The proposed method is agnostic in the sense that it does not explore the atmosphere scattering model, it makes use of convolutional networks advantage in feature extraction and transfer instead. Somewhat surprisingly, it achieves superior performance relative to all existing state-of-the-art methods for image dehazing even on SOTS outdoor images, which are synthesized using the atmosphere scattering model. In order to improve its weakness in indoor hazy images and enhance the dehazed image's visual quality, a lightweight parallel network is put forward. It employs a different convolution strategy that extracts features with larger reception field to generate a complementary image. With the help of a parallel stream, the fusion of the two outputs performs better in PSNR and SSIM than other methods. / Thesis / Master of Applied Science (MASc)

We propose an end-to-end trainable Convolutional Neural Network (CNN), named GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules: pre-processing, backbone, and post-processing. The trainable pre-processing module can generate learned inputs with better diversity and more pertinent features as compared to those derived inputs produced by hand-selected pre-processing methods. The backbone module implements a novel attention-based multi-scale estimation on a grid network, which can effectively alleviate the bottleneck issue often encountered in the conventional multi-scale approach. The post-processing module helps to reduce the artifacts in the final output. Experimental results indicate that the GridDehazeNet outperforms the state-of-the-art on both synthetic and real-world images. The proposed hazing method does not rely on the atmosphere scattering model and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned. / Thesis / Master of Applied Science (MASc)

In the simple form, a communication system includes a transmitter and a receiver. In the transmitter, it transforms the one-hot vector message to produce a transmitted signal. In general, the transmitter demands restrictions on the transmitted signal. The channel is defined by the conditional probability distribution function. On receiving of the transmitted signal with noise, the receiver appears to apply the transformation to generate the estimate of one hot vector message. We can regard this simplest communication system as a specific case of autoencoder from a deep learning perspective. In our case, autoencoder used to learn the representations of the one-hot vector which are robust to the noise channel and can be recovered at the receiver with the smallest probability of error. Our task is to make some improvements on the autoencoder systems. We propose different schemes depending on the different cases. We propose a method based on optimization of softmax and introduce the L1/2 regularization in MSE loss function for SISO case and MIMO case, separately. The simulation shows that both our optimized softmax function method and L1/2 regularization loss function have a better performance than the original neural network framework. / Thesis / Master of Applied Science (MASc)

Memoria para optar al título de Ingeniero Civil Eléctrico / La enfermedad de Parkinson es un trastorno neurodegenerativo y objeto de interés en la comunidad científica, dado que existe una mayor incidencia luego de los 60 años en promedio y además la población mundial posee una alta esperanza de vida, implicando que existirá una mayor cantidad de casos en el futuro. Los tratamientos actuales para este trastorno se componen de un generador de pulsos yelectrodos, en donde se estimula de manera constante una zona del cuerpo (puede ser un área cerebral o parte de la médula) independiente del estado actual del paciente, lo cual conlleva en algunos efectos secundarios no deseados. El presente trabajo de memoria se enfoca en esta problemática, ya que busca un lazode control para estos tratamientos y corresponde a una de las primeras aproximaciones con aprendizaje de máquinas. En particular, se estudia un clasificador de movimiento con incertidumbre a partir de la actividad neural de un modelo animal de rata de 6-OHDA. De esta manera, se realiza la extracción de movimiento a partir de un video y de señales cerebrales de un modelo animal de Parkinson a través de algoritmos de ventanas deslizantes, generando imágenes de potencia en ciertas bandas de frecuencia con una etiqueta respectiva de movimiento a partir del video. Estas imágenes sirven para entrenar a un clasificador de Deep Learning Bayesiano, el cual puede extraer incertidumbre en la clasificación. Así, al utilizar Deep Learning Bayesiano con la forma de evaluación de MC Dropout se llega a obtener un recall de 80 % para la etiqueta de movimiento y la base de datos consistente en una ventana deslizante de medio segundo. Además, esta arquitectura es superior (para esta base de datos) en comparación a la de Deep Learning y evaluación estándar de dropout. Por otro lado, para estos resultados se tiene que con una mejor clasificación se obtiene una menor incertidumbre, lo cual es una de las ventajas al usar Deep Learning Bayesiano pues permite obtener una medida de confianza en la clasificación al realizar evaluaciones estocásticas. Finalmente, cabe destacar que este trabajo puede usarse como base para obtener una estrategia de control para un circuito cerrado específico para cada paciente, el cual posee incertidumbre en predicciones implicando en la confianza que posee el sistema para cambiar un estado específico. Para generar una nueva estrategia más robusta con incertidumbre, se debería repetir este experimento agregando nuevos biomarcadores o indicadores fisiológicos, además de explorar otros algoritmos para extracción de movimiento para el etiquetado de la base de datos. / FONDECYT

Indiana University-Purdue University Indianapolis (IUPUI) / Current research in deep learning is primarily focused on using Python as a support language. Go, an emerging language, that has many benefits including native support for concurrency has seen a rise in adoption over the past few years. However, this language is not widely used to develop learning models due to the lack of supporting libraries and frameworks for model development. In this thesis, the use of Go for the development of neural network models in general and convolution neural networks is explored. The proposed study is based on a Go-CUDA implementation of neural network models called GoCuNets. This implementation is then compared to a Go-CPU deep learning implementation that takes advantage of Go's built in concurrency called ConvNetGo. A comparison of these two implementations shows a significant performance gain when using GoCuNets compared to ConvNetGo.

<p>  </p> <p>The abundance of heterogeneous data produced and collected each day via multimodal sources may contain hidden events of interest, but in order to extract them the streams of data need to be analyzed with appropriate algorithms, so these events are presented to the end user at the right moment and at the right time. This dissertation proposes a series of algorithms that shape a comprehensive framework for situational knowledge on demand to address this problem. The framework consists of several modules and approaches, each of them is presented in a separate chapter: I begin with video data analysis in streaming video and video at rest for enhanced object detection of real-life surveillance video. For detecting the rare events of interest, I develop a semantic video analysis algorithm which uses an overlay knowledge graph and a semantical network. I show that the usage of the external knowledge for understanding the semantic analysis outperforms other techniques such as transfer learning. </p> <p>The semantical outliers can be used further for improving the algorithm of detecting new objects in the stream of different modalities. I extend the framework with additional modules for natural language data and apply the extended version of the semantic analysis algorithm to define the events of interest from multimodal streaming data. I present a way of combining several feature extractors which can be extended to multiple heterogeneous streams of data in order to efficiently fuse the data based on its semantical similarity, and then show how the serverless architecture of the framework outperforms conventional cloud software architecture. </p> <p>Besides detecting the rare and semantically incompatible events, the semantic analysis can be used for improving the neural networks performance with the data augmentation. The algorithm presented for augmenting the data with the potentially novel objects to circumvent the data drift problem uses the knowledge graph and generative adversarial networks to present the objects to augment the training datasets for supervised learning. I extend the presented framework with a pipeline for generating synthetic novelties to improve the performance of feature extractors and provide the empirical evaluation of the developed method.</p>