论文标题
Stan：乳房超声图片分割的小肿瘤感知网络
Stan: Small tumor-aware network for breast ultrasound image segmentation
论文作者
论文摘要
乳腺肿瘤分割提供了准确的肿瘤边界，并作为进一步癌症定量的关键步骤。尽管已经提出并实现了有希望的结果，但现有的方法很难检测到小乳腺肿瘤。检测小肿瘤的能力对于使用计算机辅助诊断（CAD）系统的早期癌症尤为重要。在本文中，我们提出了一种称为小肿瘤感知网络（Stan）的新型深度学习结构，以提高分割不同尺寸的肿瘤的性能。新的体系结构既集成了丰富的上下文信息和高分辨率图像特征。我们在两个公共乳房超声数据集上使用七个定量指标验证了所提出的方法。提出的方法在细分小乳腺肿瘤方面的最新方法优于最先进的方法。指数
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
