Name
Abstract | ||
|---|---|---|
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 amount of 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICIP.2012.6467380 | ICIP |
Keywords | Field | DocType |
computerised tomography,content-based retrieval,image retrieval,medical image processing,radiology,visual databases,CAD systems,CBIR method,computer-aided diagnosis,computer-predicted malignancy data,content-based image retrieval,diagnostic information,diagnostically labeled datasets,medical image database,performance improvement,pulmonary CT scans,pulmonary computed tomography scans,radiologist-predicted malignancy data,undiagnosed lung nodules,undiagnosed query nodules,unlabeled image annotation,Computer-aided diagnosis,biomedical imaging,cancer detection,semi-supervised learning | CAD,Computer vision,Pattern recognition,Computer science,Image retrieval,Ground truth,Artificial intelligence,Computed tomography,Cad system,Labeled data,Medical diagnosis,Content-based image retrieval | Conference |
ISSN | Citations | PageRank |
1522-4880 | 5 | 0.51 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anne-Marie Giuca | 1 | 11 | 0.99 |
| Kerry A. Seitz | 2 | 5 | 0.51 |
| Jacob D. Furst | 3 | 545 | 56.63 |
| Daniela Stan Raicu | 4 | 469 | 46.22 |