Stan: Small tumor-aware network for breast ultrasound image segmentation
This addresses the challenge of detecting early-stage cancers in computer-aided diagnosis systems for breast ultrasound, representing a domain-specific incremental improvement.
The paper tackled the problem of segmenting small breast tumors in ultrasound images, which is difficult for existing deep learning methods, and proposed a novel architecture called STAN that outperformed state-of-the-art approaches on two public datasets.
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