Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models\u27 predictions. Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a database of 2258 cases mixed from multiple institutions. The generalization of the models was tested on second set of 2177 cases. Clusters were identified in the database using a priori knowledge and unsupervised learning methods (agglomerative hierarchical clustering followed by K-Means, SOM, AutoClass). The performance of the global models over the clusters was examined and local models were trained for clusters. While some local models were superior to some global models, we were unable to build a modular CAD system that was better than the global BP-ANN model. The ensemble systems based on simplistic combination schemes did not result in significant improvements and more complicated combination schemes were found to be unduly optimistic. One of the most striking results of this dissertation was that CAD systems trained on a mixture of lesion types performed much better on masses than on calcifications. Our study of the institutional effects suggests that models built on cases mixed between institutions may overcome some of the weaknesses of models built on cases from a single institution. It was suggestive that each of the unsupervised methods identified a cluster of younger women with well-circumscribed or obscured, oval-shaped masses that accounted for the majority of the BP-ANN’s recommendations for follow up. From the cluster analysis and the CART models, we determined a simple diagnostic rule that performed comparably to the global BP-ANN. Approximately 98% sensitivity could be maintained while providing approximately 26% specificity. This should be compared to the clinical status quo of 100% sensitivity and 0% specificity on this database of indeterminate cases already referred to biopsy

STATIONARY DIGITAL TOMOSYNTHESIS: IMPLEMENTATION, CHARACTERIZATION, AND IMAGE PROCESSING TECHNIQUES

The use of carbon nanotube cathodes for x-ray generation was pioneered and perfected by our team in the Applied Nanotechnology Laboratory at the University of North Carolina at Chapel Hill. Over the past decade, carbon nanotube (CNT) field emission x-ray source technology has matured and translated into multiple pre-clinical and clinical devices. One of the most prominent implementations of CNT x-ray technology is a limited angle tomography method called tomosynthesis, which is rapidly emerging in clinical radiography. The purpose of this project is two-fold, to develop and characterize to the latest iteration, stationary intraoral tomosynthesis, and develop a low-dose, effective scatter reduction technique for breast and chest tomosynthesis. The first portion of this project was to develop and evaluate a new quasi-3D imaging modality for dental imaging. My work consists of experiments which dictated the design parameters and subsequent system evaluation of the dedicated s-IOT clinical prototype system currently installed in the UNC Department of Oral and Maxillofacial Radiology clinic in the School of Dentistry. Experiments were performed in our lab to determine optimal source array geometry and system configuration. The system was fabricated by our commercial partner then housed in our research lab where I performed initial characterization and assisted with software development. After installation in the SOD, I performed additional system characterization, including source output validation, dosimetry, and quantification of resolution. The system components and software were refined through a rapid feedback loop with the engineers involved. Four pre-clinical imaging studies have been performed in collaboration with several dentists using phantoms, extracted teeth, and cadaveric dentition. I have generated an operating manual and trained four dental radiologists in the use of the s-IOT device. The system has now been vetted and is ready for patient use. The second portion of this project consists of hardware development and implementation of an image processing technique for scatter correction. The primary sampling scatter correction (PSSC) is a beam pass technique to measure the primary transmission through the patient and calculate the scatter profile for subtraction. Though developed for breast and chest tomosynthesis, utilization in mammography and chest radiography are also demonstrated in this project. This dissertation is composed of five chapters. Chapters one and two provide the basics of x-ray generation and a brief history of the evolution of carbon nanotube x-ray source technology in our lab at UNC. Chapter three focuses on stationary intraoral tomosynthesis. The first section provides background information on dental radiology and project motivation. Sections 3.2 and 3.3 detail my work in benchtop feasibility and optimization studies, as well as characterization and evaluation of the clinical prototype. Chapter four introduces scatter in imaging, providing motivation for my work on primary sampling scatter correction (PSSC) image processing method, detailed in chapter five.Doctor of Philosoph