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Regular breast screening with mammography allows for early detection of cancer and reduces breast cancer mortality. However, significant false positive and false negative rates leave opportunities for improving diagnostic accuracy. Computer-aided detection (CAD) softwares have been available to radiologists for decades to address these issues. However, traditional CAD products have failed to improve interpretation of full-field digital mammography (FFDM) images in clinical practice due to low sensitivity and a large number of false positives per image. Usage of deep learning models have shown promise in improving performance of radiologists. Unfortunately, they still have a large amount of false positives per images at reasonable sensitivities. In this work, we propose a simple and intuitive two-stage detection framework, named WRDet. WRDet consists of two stages: a region proposal network that has been optimized to enhance sensitivity and a second-stage patch classifier that boosts specificity. We highlight different rules for matching predicted proposals and ground truth boxes that are commonly used in the mammography CAD literature and compare these rules in light of the high variability in quality of ground truth annotations of mammography datasets. We additionally propose a new criterion to match predicted proposals with loose bounding box annotations that is useful for two-stage CAD systems like WRDet. Using the common CAD matching criterion that considers a prediction true positive if its center falls within the ground truth annotation, our system achieves an overall sensitivity of 81.3% and 89.4% at 0.25 and 1 false positive mark per image, respectively. For the task of mass detection, we achieve a sensitivity of 85.3% and 92% at 0.25 and 1 false positive mark per image, respectively. We also compare our results with select models reported in literature using different matching criteria. Our results demonstrate the possibility of a CAD system that could be beneficial in improving accuracy of screening mammography worldwide.
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