Machine Learning
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Current computer-aided detection (CAD) systems for mammography screening work as prompting devices that aim at drawing radiologists' attention to suspicious regions. In this paper, we investigate utilizing a CAD system based on a support vector machine classifier as a standalone tool for recalling additional abnormal cases missed at screening, while keeping the associated recall rate at low levels. We tested the system on a large database of 5800 cases containing abnormal instances (1%) corresponding to prior examinations missed at screening. The results showed that 26% of the missed cases could be detected with a low additional recall rate of 2%. Moreover, after extrapolating this result to a screening program, we determined that, with our system, 0.73 additional cancers per 20 additional recalls could be potentially detected. We also compared the proposed system with a regular CAD system intended for non-standalone operation. The performance of the proposed system was significantly better.