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Title: DEVELOPMENT OF COMPUTER-AIDED DIAGNOSIS METHODS IN MAMMOGRAPHY
Creator: Wang, Juan
Date: 2015, 2015-12
Description: Computer-aided diagnosis (CAD) is developed as a diagnostic aid to provide a “second opinion” in diagnosis of breast cancer in early stage....
Show moreComputer-aided diagnosis (CAD) is developed as a diagnostic aid to provide a “second opinion” in diagnosis of breast cancer in early stage. Clustered microcalcifications (MCs) can be an important early sign of breast cancer. The goal of this work is to develop automatic CAD methods in mammography for breast cancer. Its contribution consists of both development of machine learning algorithms and study of related issues in detection and diagnosis of breast cancer with clustered MCs. First, a bi-thresholding scheme is proposed for reduction of false-positives (FPs) associated with linear structures in MC detection. An unified classifier with dummy variable modeling is further developed to reduce the FPs caused by both linear structures and MC-like noise patterns. It is demonstrated that both of the proposed algorithms can reduce FPs in MC detection, and thus, improve the detection accuracy significantly. Second, a spatial density modeling approach is investigated to quantify the spatial distribution of the MCs in a cluster when the MC detection is inaccurate. A spatial density function (SDF) is defined such that the extracted features are more robust to the presence of FPs and false-negatives (FNs) in MC detection. The results show that the features extracted from the SDF can achieve better class separation while being robust to the variations in MC detection when compared with those extracted from a traditional region-based method. Third, a retrieval-boosted approach is studied to discriminate between the benign and malignant MC lesions. A retrieval strategy is proposed to boost the classification performance by taking into account the similarity both in image features and in pathology. An adaptive Adaboost classifier, which can be adapted to the retrieved cases at a low computational cost, is applied to demonstrate the benefit of the retrieval strategy. The results show that the retrieval-boosted approach can signifishow that the features extracted from the SDF can achieve better class separation while being robust to the variations in MC detection when compared with those extracted from a traditional region-based method. Third, a retrieval-boosted approach is studied to discriminate between the benign and malignant MC lesions. A retrieval strategy is proposed to boost the classification performance by taking into account the similarity both in image features and in pathology. An adaptive Adaboost classifier, which can be adapted to the retrieved cases at a low computational cost, is applied to demonstrate the benefit of the retrieval strategy. The results show that the retrieval-boosted approach can significantly outperform its baseline classifier and that inclusion of pathology information in the retrieval can further improve the classification accuracy. Fourth, the perceptual similarity of MC lesions by radiologists is studied. The issues investigated include the degree of variability in the similarity ratings, the impact of this variability on agreement between readers in retrieval of similar lesions, and the factors contributing to the readers’ similarity ratings. The results indicate that perceptually similar lesions could be of diagnostic value in diagnosis for clustered MCs. Fifth, the feasibility of modeling the perceptual similarity of MC lesions is investigated. A support vector regression (SVR) is applied to model the perceptual similarity of clustered MCs, and a feature saliency analysis derived from SVR is used to determine the most relevant image features among a large set of candidate features. The results demonstrate that the relevant features are consistent in radiologists’ similarity ratings among different MC lesions, indicating that the perceptual similarity of MC lesions by radiologists can be effectively modeled. Finally, whether retrieval of similar images can effectively assist radiologists in diagnosis of clustered MCs is investigated. A retrieval system for relevant images is designed by considering both perceptually similar image features and the likelihood of malignancy of the lesion under consideration. An observer study is conducted to evaluate the diagnostic value of the proposed retrieval system. The results indicate that the proposed retrieval system has the potential to improve the reader’s ability in diagnosis of breast cancer with clustered MCs.
Ph.D. in Electrical Engineering, December 2015
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Title: TEMPORAL AND SPATIOTEMPORAL MODELS FOR SHORT-CRIME PREDICTION
Creator: Liu, Xiaomu
Date: 2017, 2017-07
Description: One of the most important aspects of predictive policing is identifying the likely time and place of crime occurrences so as to prevent future...
Show moreOne of the most important aspects of predictive policing is identifying the likely time and place of crime occurrences so as to prevent future crimes. The ability to make short-term predictions may be of particular importance for optimizing police resource allocation. The goal of this study is to investigate the temporal and spatiotemporal pattern of crime in the city of Chicago and to build corresponding predictive models. First, a temporal model for forecasting citywide violent crime time count is proposed. This model is composed of a long-term trend and short-term variations using data of time, weather and crime. The importance of model reproducibility is addressed in this study to produce low-complexity models. We introduce an approach that provides a way to extend the model selection criterion to both prediction accuracy and model reproducibility. The experimental results show that models produced by this approach outperform several simple time-series models. It is also found that these models typically include fewer variables; therefore, they are more interpretable, and may provide superior generalization error. Next we develop a framework that provides predictions for tomorrow’s violent crime counts at the level of a police district. The procedures include citywide daily violent crime count prediction, violent crime density estimation, and distributing citywide predictions to districts according to the estimated densities. In order to estimate the crime spatial densities, we use mesh modeling and demonstrate that a mesh model can be used as the structure for modeling the spatial variation of crime rate since it is well adapted to the inhomogeneous crime distribution. The experimental results show that our method provides more-accurate forecasts than those given by historical crime statistics. One aspect of studying spatial pattern of crimes is identifying geographical regions with similar crime characteristics. Specifically, we illustrate applying unsupervised clustering techniques to segment the city into sub-regions. We explore the use of Gaussian mixture models combined with a Markov random field for the purpose of regularization. We also propose a framework for the evaluation of clustering models without knowing the ground truth, which can present a more-complete picture for model selection in unsupervised clustering problems. Finally, we develop a spatiotemporal prediction method that predicts the locations where violent crimes or property crimes are most likely to occur tomorrow. Crime incidents are rasterized by a spatiotemporal grid. Other factors that affect the time and location preferences of criminal activities are also leveraged and represented by that grid. Each spatiotemporal grid cell is treated as an example for training and testing our models. We also explore whether pooling data from various sub-regions based on spatial clustering can improve model performance. The experimental results show that our models are more accurate than conventional hot-spot models. It is found that the effects of using different training samples are not consistent, which depends on target crime type.
Ph.D. in Electrical Engineering, July 2017
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Title: Machine learning applications to video surveillance camera placement and medical imaging quality assessment
Creator: Lorente Gomez, Iris
Date: 2022
Description: In this work, we used machine learning techniques and data analysis to approach two applications. The first one, in collaboration with the...
Show moreIn this work, we used machine learning techniques and data analysis to approach two applications. The first one, in collaboration with the Chicago Police Department (CPD), involves analyzing and quantifying the effect that the installation of cameras had on crime, and developing a predictive model with the goal of optimizing video surveillance camera location in the streets. While video surveillance has become increasingly prevalent in policing, its intended effect on crime prevention has not been comprehensively studied in major cities in the US. In this study, we retrospectively analyzed the crime activities in the vicinity of 2,021 surveillance cameras installed between 2005 and 2016 in the city of Chicago. Using Difference-in-Differences (DiD) analysis, we examined the daily crime counts that occurred within the fields-of-view of these cameras over a 12-month period, both before and after the cameras were installed. We also investigated their potential effect on crime displacement and diffusion by examining the crime activities in a buffer zone (up to 900 ft) extended from the cameras. The results show that, collectively, there was an 18.6% reduction in crime counts within the direct viewsheds of all of the study cameras (excluding District 01 where the Loop -Chicago's business center- is located). In addition, we adapted the methodology to quantify the effect of individual cameras. The quantified effect on crime is the prediction target of our 2-stage machine learning algorithm that aims to estimate the effect that installing a videocamera in a given location will have on crime. In the first stage, we trained a classifier to predict if installing a videocamera in a given location will result in a statistically significant decrease in crime. If so, the data goes through a regression model trained to estimate the quantified effect on crime that the camera installation will have. Finally, we propose two strategies, using our 2-stage predictive model, to find the optimal locations for camera installations given a budget. Our proposed strategies result in a larger decrease in crime than a baseline strategy based on choosing the locations with higher crime density.The second application that forms this thesis belongs to the field of model observers for medical imaging quality assessment. With the advance of medical imaging devices and technology, there is a need to evaluate and validate new image reconstruction algorithms. Image quality is traditionally evaluated by using numerical figures of merit that indicate similarity between the reconstruction and the original. In medical imaging, a good reconstruction strategy should be one that helps the radiologist perform a correct diagnosis. For this reason, medical imaging reconstruction strategies should be evaluated on a task-based approach by measuring human diagnosis accuracy. Model observers (MO) are algorithms capable of acting as human surrogates to evaluate reconstruction strategies, reducing significantly the time and cost of organizing sessions with expert radiologists. In this work, we develop a methodology to estimate a deep learning based model observer for a defect localization task using a synthetic dataset that simulates images with statistical properties similar to trans-axial sections of X-ray computed tomography (CT). In addition, we explore how the models access diagnostic information from the images using psychophysical methods that have been previously employed to analyze how the humans extract the information. Our models are independently trained for five different humans and are able to generalize to images with noise statistic backgrounds that were not seen during the model training stage. In addition, our results indicate that the diagnostic information extracted by the models matches the one extracted by the humans.
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Title: SOLID-STATE SMART PLUG DEVICE
Creator: Deng, Zhixi
Date: 2022
Description: Electrical faults are a leading cause of residential fire, and flexible power cords are particularly susceptible to metal or insulation...
Show moreElectrical faults are a leading cause of residential fire, and flexible power cords are particularly susceptible to metal or insulation degradation that may lead to a variety of electrical faults. Smart Plugs are a type of plug-in device controlling electrical loads via wireless communication for consumer market. However, there is lack of circuit protection features in existing Smart Plug products. Moreover, there is no previous product or research on Smart Plug with circuit protection features. This thesis introduces a new Smart Plug 2.0 concept which offers all-in-one protection against over-current, arc, and ground faults in addition to the smart features in Smart Plug products. It aims at preventing fire and shock hazards caused by degraded or damaged power cords and electrical connections in homes and offices. It offers microsecond-scale time resolution to detect and respond to a fault condition, and significantly reduces the electrothermal stress on household electrical wires and loads. A new arc fault detection method is developed using machine learning models based on load current di/dt events. The Smart Plug 2.0 concept has been validated experimentally. A 120V/10A solid-state Smart Plug 2.0 prototype using power MOSEFTs is designed and tested. It has experimentally demonstrated the comprehensive protection features against all types of electrical faults.
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