Automated Labeling of Screening Mammograms with Arterial Calcifications

For the automatic detection of malignant microcalcification clusters in screening mammograms a computer aided detection (CADe) system has been developed. The most frequent false positives of this system are breast arterial calcifications (BACs). The purpose of this study was to construct a method for selecting cases with BACs in mammographic screening data as part of a procedure to reduce false positives of the CADe system. To automatically select cases containing BACs, a GentleBoost classifier was trained. For composing the training set, the CADe system was applied on 10,000 normal cases. From these cases, 400 cases with the most significant false positives were included in the training set and an additional 200 cases with less obvious false positives. For testing, an independent test set was created by cluster detection of 1,000 normal cases and 95 malignant cases. After cluster detection 342 normal cases contained false positives and in 93 malignant cases true positive clusters were detected. In the training set, 244 cases showed signs of BACs and in the test set 95 cases. A total of 102 case-based features were calculated to train the classifier. A ROC curve was calculated of the classification of the test set bootstrapped 5000 times. The area under the curve of the ROC was 0.92 and already 44% of the cases with BACs were detected without any false positives. Furthermore, 90% of the cases with BACs were detected at a false positive rate of 20%. The performance of the proposed selection method implies a good feasibility to classify cases with BACs at high specificity. By using this selection we will be able to apply dedicated methods for false positive reduction due to BACs.