|
Computer-aided diagnosis (CAD) has gained considerable attention for breast cancer screening owing to its high diagnostic efficiency and satisfactory accuracy. However, it has been revealed that traditional CAD systems for mammography are vulnerable to dense breast tissue, which could hide underlying tumors. To resolve this issue, we devised a learning scheme that equips the U-Net backbone with a well-designed attention mechanism to suppress the over-detection rate for nongland mammary regions in dense breast tissue and applied to the CAD for breast ultrasound (BUS) images. The proposed method has two stages: initial mammary gland segmentation, which involves the selection of a region in the mammary gland where a tumor may occur; then tumor region segmentation, wherein the attention U-Net detects tumor regions by characterizing the selected mammary gland probability map as a spatial attention map, drawing selective attention to mammary gland tissues. We evaluated the proposed tumor detection scheme on several public BUS image datasets. Comparative results demonstrate that the proposed approach achieves the best performance in most conditions. Notably, when considering the percentage of all actual tumors that were correctly segmented, the proposed method showed a tumorwise accuracy performance of 92.7%.
|
