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Proceedings Paper

Improving positive predictive value in computer-aided diagnosis using mammographic mass and microcalcification confidence score fusion based on co-location information
Author(s): Seung Hyun Lee; Dae Hoe Kim; Jae Young Choi; Yong Man Ro
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Paper Abstract

In this study, a novel fusion framework has been developed to combine the detection of both breast masses and microcalcifications (MCs), aiming to improve positive predictive value (PPV) in Computer-aided Diagnosis (CADx). Clinically, it has been widely accepted that a mass associated with MC is a useful indicator of predicting the malignancy of the mass. In light of this fact, given that a mass and MCs are co-located each other (i.e., they are at the same location), the proposed fusion framework combines confidence scores of the mass and MCs for the purpose of improving the probability that the mass is malignant. To this end, the popular Bayesian network model is applied to effectively combine the detection confidence scores and to achieve higher accuracy for malignant mass classification. To demonstrate the effectiveness of the proposed fusion framework, 31 mammograms were collected from the public DDSM database. The proposed fusion framework can increase the area under the receiver operating characteristic curve (AUC) from 0.7939 to 0.8806, and the partial area index (PAUC) above the sensitivity of 0.9 from 0.1270 to 0.2280, compared to the CADx system without exploiting co-location information with MCs. Based on these results, it can be expected that the proposed fusion framework can be readily applied for realizing CADx systems with the higher PPV.

Paper Details

Date Published: 28 February 2013
PDF: 4 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701Y (28 February 2013); doi: 10.1117/12.2007771
Show Author Affiliations
Seung Hyun Lee, KAIST (Korea, Republic of)
Dae Hoe Kim, KAIST (Korea, Republic of)
Jae Young Choi, KAIST (Korea, Republic of)
Yong Man Ro, KAIST (Korea, Republic of)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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