Efficient learning in computer-aided diagnosis through label propagation.

Computer-Aided Diagnosis (CADx) systems can be used to provide second opinions in the medical diagnostic process. These CADx systems are expensive to build as they require a large amount of correctly labeled example data. In order to ensure the accuracy of a training label, a radiograph may be assessed by multiple radiologists, increasing the time and money necessary to build these diagnostic systems. In this paper, we minimize the cost necessary to train CADx systems while accounting for unreliable labels by reducing label uncertainty. We introduce a method which reduces the cost required to build a CADx system while improving the overall accuracy and demonstrate it on the Lung Image Database Consortium (LIDC) database. We exploit similarities between images by clustering image features of lung nodule CT scans and propagating a single label throughout the cluster. By informatively choosing better labels through clustering, this method achieves a stronger accuracy (5.2% increase) while using fewer labels (29% less) compared to a state of the art label saving technique designed for this medical dataset.