Expanding diagnostically labeled datasets using content-based image retrieval

In computer-aided diagnosis (CAD), having an accurate ground truth is critical. However, the number of databases containing medical images with diagnostic information is limited. Using pulmonary computed tomography (CT) scans, we develop a content-based image retrieval (CBIR) approach to exploit the limited images with diagnostically labeled data in order to annotate unlabeled images with diagnoses. By applying this CBIR method iteratively, we expand the set of diagnosed data available for CAD systems. We evaluate the method by implementing a CAD system that uses undiagnosed lung nodules as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system, radiologist- and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems.