Abstract
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.
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