  Breast tumor segmentation provides accurate tumor boundary, and serves as a
key step toward further cancer quantification. Although deep learning-based
approaches have been proposed and achieved promising results, existing
approaches have difficulty in detecting small breast tumors. The capacity to
detecting small tumors is particularly important in finding early stage cancers
using computer-aided diagnosis (CAD) systems. In this paper, we propose a novel
deep learning architecture called Small Tumor-Aware Network (STAN), to improve
the performance of segmenting tumors with different size. The new architecture
integrates both rich context information and high-resolution image features. We
validate the proposed approach using seven quantitative metrics on two public
breast ultrasound datasets. The proposed approach outperformed the
state-of-the-art approaches in segmenting small breast tumors. Index
