Journal
2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020)
Volume -, Issue -, Pages 1469-1473Publisher
IEEE
DOI: 10.1109/isbi45749.2020.9098691
Keywords
breast ultrasound; small tumor segmentation; deep learning; multi-scale features; STAN
Funding
- Center for Modeling Complex Interactions (CMCI) at the University of Idaho through NIH [P20GM104420]
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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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