| The research presented in this dissertation represents an innovative application of computer-aided diagnosis and signal detection theory to the specific task of early detection of breast cancer in the context of screening mammography. A number of automated schemes have been developed in our laboratory to detect masses and clustered microcalcifications in digitized mammograms, on the one hand, and to classify known lesions as malignant or benign, on the other. The development of fully automated classification schemes is difficult, because the output of a detection scheme will contain false-positive detections in addition to detected malignant and benign lesions, resulting in a three-class classification task. Researchers have so far been unable to extend successful tools for analyzing two-class classification tasks, such as receiver operating characteristic (ROC) analysis, to three-class classification tasks.; The goals of our research were to use Bayesian artificial neural networks to estimate ideal observer decision variables to both detect and classify clustered microcalcifications and mass lesions in mammograms, and to derive substantial theoretical results indicating potential avenues of approach toward the three-class classification task. Specifically, we have shown that an ideal observer in an N-class classification task achieves an optimal ROC hypersurface, just as the two-class ideal observer achieves an optimal ROC curve; and that an obvious generalization of a well-known two-class performance metric, the area under the ROC curve, is not useful as a performance metric in classification tasks with more than two classes.; This work is significant for three reasons. First, it involves the explicit estimation of feature-based (as opposed to image-based) ideal observer decision variables in the tasks of detecting and classifying mammographic lesions. Second, it directly addresses the three-class classification task of distinguishing malignant lesions, benign lesions, and false-positive computer detections. Finally, it develops important theoretical results for N-class classification tasks that should prove of value in the development of a three-class extension to ROC analysis methods. |
