A new scheme to evaluate the accuracy of knowledge representation in automated breast cancer diagnosis

In the field of breast cancer diagnosis, computer-aided diagnosis (CAD) systems can provide doctors important second opinions, relying on the advanced computation ability and artificial intelligence of computing systems. The collaboration between doctors and CAD systems can help reducing the false diagnosis rate. Knowledge representation is an important chain for any artificial intelligence system, including automated breast cancer diagnosis. The breast cancer experts´ knowledge of distinguishing benign and malignant lesions is well described by Breast Imaging Reporting and Data System (BIRADS). Many digital formulas have been proposed to quantify BIRADS features. However, there is no direct evaluation scheme for these digital features. A common way that people evaluate digital features is using them as the input for classifiers, such as machine learning methods, and then evaluating the performance of classifiers, which indirectly serves as the evaluation of digital features. The performance of a classifier is affected by the digital features, but also affected by other factors. It is inaccurate to use only the performance of classifiers as the metric to evaluate digital features. The vision of this work is to separate the evaluation of digital features from the evaluation of classifiers, with the purpose of providing an accurate feature measurement procedure and improving the quality of knowledge representation. An independent feature evaluation scheme without using any automatic classifier is proposed. Such a scheme can directly evaluate how precisely experts´ knowledge is represented in computerized systems. Several commonly used digital features and newly proposed digital features in this work are evaluated using this scheme on a breast ultrasound image database. Pathological results and radiologist´s opinions serve as the ground truth for evaluation purpose.