论文标题
乳腺癌筛查的区分局部稀疏表示
Discriminative Localized Sparse Representations for Breast Cancer Screening
论文作者
论文摘要
乳腺癌是发达国家和发展中国家女性中最常见的癌症。乳腺癌的早期发现和诊断可能会降低其死亡率并改善生活质量。计算机辅助检测（CADX）和计算机辅助诊断（CAD）技术显示出有望减轻人类专家阅读的负担并提高结果的准确性和可重复性。稀疏分析技术已产生相关的结果来表示和识别成像模式。在这项工作中，我们提出了一种标记一致的空间局部整体稀疏分析（LC-SLESA）的方法。在这项工作中，我们将词典学习应用于基于基础的稀疏分析方法，以将乳腺病变归类为良性或恶性肿瘤。我们的方法与LC-KSVD词典学习结合使用了MIAS数据集上的10倍，20倍和30倍的交叉验证评估。我们的结果表明，提出的稀疏分析可能是乳腺癌筛查应用的有用组成部分。
Breast cancer is the most common cancer among women both in developed and developing countries. Early detection and diagnosis of breast cancer may reduce its mortality and improve the quality of life. Computer-aided detection (CADx) and computer-aided diagnosis (CAD) techniques have shown promise for reducing the burden of human expert reading and improve the accuracy and reproducibility of results. Sparse analysis techniques have produced relevant results for representing and recognizing imaging patterns. In this work we propose a method for Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA). In this work we apply dictionary learning to our block based sparse analysis method to classify breast lesions as benign or malignant. The performance of our method in conjunction with LC-KSVD dictionary learning is evaluated using 10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results indicate that the proposed sparse analyses may be a useful component for breast cancer screening applications.
