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首页> 外文会议>International Congress on Image and Signal Processing, BioMedical Engineering and Informatics >The pseudo-label scheme in breast tumor classification based on BI-RADS features
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The pseudo-label scheme in breast tumor classification based on BI-RADS features

机译：基于BI-RADS特征的乳腺肿瘤分类中的伪标签方案

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摘要

The proposed method employs the Breast Imaging Reporting and Data System (BI-RADS) feature to classify the breast tumor. Compared with the ultrasound breast tumor classification methods based on the image, the “semantic gap” between the clinical feature and image feature is eliminated. In order to address the shortage of the labeled data, the pseudo-labeled scheme based on SVM is designed. The SVM classifier is trained by few labeled samples, and the hybrid dataset which contains the pseudo-labeled sample marked by SVM and few labeled samples is adopted to train the decision tree. 500 ultrasound breast tumor cases are collected to evaluate the proposed method. According to the result of the experiment, compared with the decision tree trained by the labeled dataset only, the accuracy of decision tree train by hybrid dataset improves 2.65%, the NPV improves 7.00%, and the Sensitivity increases 3.30%.
机译：该方法采用乳房成像报告和数据系统（BI-RADS）特征来分类乳腺肿瘤。与基于图像的超声乳房肿瘤分类方法相比，消除了临床特征与图像特征之间的“语义间隙”。为了解决标记数据的短缺，设计了基于SVM的伪标记方案。 SVM分类器由少量标记的样本培训，并且包含包含由SVM标记的伪标记样本和少量标记样品的混合数据集被采用培训决策树。收集500种超声乳腺肿瘤案例以评估所提出的方法。根据实验结果，与由标记数据集训练的决策树相比，混合数据集的决策树列车的准确性提高了2.65 ％，NPV提高了7.00 ％，灵敏度增加3.30 ％。

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