Breast Anatomy Enriched Tumor Saliency Estimation
This work addresses the problem of improving tumor localization accuracy in breast ultrasound for medical diagnosis, representing an incremental advancement in domain-specific detection methods.
The paper tackled the challenge of tumor detection in breast ultrasound images by proposing a novel tumor saliency estimation model guided by enriched breast anatomy knowledge, which increased F-measure by 10% on a public dataset compared to state-of-the-art models.
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.