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 CT scans, we develop two independent methods, one using content-based image retrieval (CBIR) and the other using multiple linear regression (MLR), to exploit the limited amount of diagnostically labeled data in order to annotate unlabeled images with diagnoses. By applying each of these methods iteratively, we expand the set of diagnosed data available for CAD systems. We evaluate both methods by implementing a CAD system that uses undiagnosed nodules as queries and retrieves similar nodules from the diagnostically labeled dataset, using radiologist- and computerpredicted malignancy data as ground truth for the undiagnosed query nodules in calculating precision. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems. Index Terms — One, two, three, four, five 1