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Finally, we use transfer learning to assist the training process and to improve the robustness of the model ulteriorly. On the INbreast dataset, each image has an average of 0.495 false positives whilst the recall rate is 0.930; On the DDSM dataset, when each image has 0.599 false positives, the recall rate reaches 0.943. The experimental results on datasets INbreast and DDSM show that the proposed BMassDNet can obtain competitive detection performance over the current top ranked methods. The experimental results on datasets INbreast an
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