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DEVELOPMENT OF COMPUTER-AIDED DIAGNOSIS METHODS IN MAMMOGRAPHY
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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TEMPORAL AND SPATIOTEMPORAL MODELS FOR SHORT-CRIME PREDICTION
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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