@inproceedings{d6745fb41f984afc824707595408104d,
title = "The pseudo-label scheme in breast tumor classification based on BI-RADS features",
abstract = "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%.",
keywords = "BI-RADS, Breast tumors classification, Decision tree, Pseudo-label scheme",
author = "Fan Zhang and Qinghua Huang and Xuelong Li",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 ; Conference date: 14-10-2017 Through 16-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CISP-BMEI.2017.8302288",
language = "英语",
series = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
editor = "Qingli Li and Lipo Wang and Mei Zhou and Li Sun and Song Qiu and Hongying Liu",
booktitle = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
}