School of Computer Engineering and Science, Shanghai University, Shanghai, People's Republic of China.
Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai, People's Republic of China.
Phys Med Biol. 2020 Dec 2;65(24). doi: 10.1088/1361-6560/abc5c7.
Breast cancer is one of the leading causes of female cancer deaths. Early diagnosis with prophylactic may improve the patients' prognosis. So far ultrasound (US) imaging has been a popular method in breast cancer diagnosis. However, its accuracy is bounded to traditional handcrafted feature methods and expertise. A novel method, named dual-sampling convolutional neural networks (DSCNNs), was proposed in this paper for the differential diagnosis of breast tumors based on US images. Combining traditional convolutional and residual networks, DSCNN prevented gradient disappearance and degradation. The prediction accuracy was increased by the parallel dual-sampling structure, which can effectively extract potential features from US images. Compared with other advanced deep learning methods and traditional handcrafted feature methods, DSCNN reached the best performance with an accuracy of 91.67% and an area under curve of 0.939. The robustness of the proposed method was also verified by using a public dataset. Moreover, DSCNN was compared with evaluation from three radiologists utilizing US-BI-RADS lexicon categories for overall breast tumors assessment. The result demonstrated that the prediction sensitivity, specificity and accuracy of the DSCNN were higher than those of the radiologist with 10 year experience, suggesting that the DSCNN has the potential to help doctors make judgements in clinic.
乳腺癌是女性癌症死亡的主要原因之一。早期诊断和预防性治疗可能会改善患者的预后。到目前为止，超声（US）成像已成为乳腺癌诊断的一种常用方法。然而，其准确性受到传统手工特征方法和专业知识的限制。本文提出了一种新的方法，称为双采样卷积神经网络（DSCNN），用于基于 US 图像对乳腺肿瘤进行鉴别诊断。DSCNN 结合了传统卷积和残差网络，防止了梯度消失和退化。通过并行双采样结构提高了预测准确性，能够有效地从 US 图像中提取潜在特征。与其他先进的深度学习方法和传统手工特征方法相比，DSCNN 的性能最佳，准确率为 91.67%，曲线下面积为 0.939。还使用公共数据集验证了该方法的稳健性。此外，还利用 US-BI-RADS 词汇类别对整体乳腺肿瘤评估，将 DSCNN 与三位有 10 年经验的放射科医生的评估进行了比较。结果表明，DSCNN 的预测敏感性、特异性和准确性均高于具有 10 年经验的放射科医生，表明 DSCNN 有可能帮助医生进行临床诊断。
