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Ultrasound Guided Diffuse Optical Tomography For Breast Cancer Diagnosis: Algorithm Development, K M Shihab Uddin
Ultrasound Guided Diffuse Optical Tomography For Breast Cancer Diagnosis: Algorithm Development, K M Shihab Uddin
McKelvey School of Engineering Theses & Dissertations
According to National Breast Cancer Society, one in every eight women in United States is diagnosed with breast cancer in her lifetime. American Cancer Society recommends a semi-annual breast-cancer screening for every woman which can be heavily facilitated by the availability of low-cost, non-invasive diagnostic method with good sensitivity and penetration depth. Ultrasound (US) guided Diffuse Optical Tomography (US-guided DOT) has been explored as a breast-cancer diagnostic and screening tool over the past two decades. It has demonstrated a great potential for breast-cancer diagnosis, treatment monitoring and chemotherapy-response prediction. In this imaging method, optical measurements of four different wavelengths are …
A Generalized Gaussian Process Likelihood For Psychometric Function Estimation, Jonathan Wenhan Chen
A Generalized Gaussian Process Likelihood For Psychometric Function Estimation, Jonathan Wenhan Chen
McKelvey School of Engineering Theses & Dissertations
Psychometric functions model the relationship between a physical phenomenon, an independent variable, and a subject’s performance on a cognitive task. The estimation of these psychometric functions is critical for the understanding of perception and cognition as well as for the diagnosis and treatment of many sensory conditions. The ability to estimate psychometric functions of any complexity is necessary to this end. In the following thesis, a generalized likelihood function for psychometric function estimation with Gaussian processes is described and validated. Such a likelihood function is necessary to enable the usage of Gaussian processes for the estimation of non-zero guess and …
Investigating Patterns In Convolution Neural Network Parameters Using Probabilistic Support Vector Machines, Yuqiu Zhang
Investigating Patterns In Convolution Neural Network Parameters Using Probabilistic Support Vector Machines, Yuqiu Zhang
McKelvey School of Engineering Theses & Dissertations
Artificial neural networks(ANNs) are recognized as high-performance models for classification problems. They have proved to be efficient tools for many of today's applications like automatic driving, image and video recognition and restoration, big-data analysis. However, high performance deep neural networks have millions of parameters, and the iterative training procedure thus involves a very high computational cost. This research attempts to study the relationships between parameters in convolutional neural networks(CNNs). I assume there exists a certain relation between adjacent convolutional layers and proposed a machine learning model(MLM) that can be trained to represent this relation. The MLM's generalization ability is evaluated …