  Breast cancer investigation is of great significance, and developing tumor
detection methodologies is a critical need. However, it is a challenging task
for breast ultrasound due to the complicated breast structure and poor quality
of the images. In this paper, we propose a novel tumor saliency estimation
model guided by enriched breast anatomy knowledge to localize the tumor.
Firstly, the breast anatomy layers are generated by a deep neural network. Then
we refine the layers by integrating a non-semantic breast anatomy model to
solve the problems of incomplete mammary layers. Meanwhile, a new background
map generation method weighted by the semantic probability and spatial distance
is proposed to improve the performance. The experiment demonstrates that the
proposed method with the new background map outperforms four state-of-the-art
TSE models with increasing 10% of F_meansure on the BUS public dataset.
