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Few-shot decision tree for diagnosis of ultrasound breast tumor using BI-RADS features
Huang, Qinghua1,2,3; Zhang, Fan2; Li, Xuelong4
Department光学影像学习与分析中心
2018-11-01
Source PublicationMultimedia Tools and Applications
ISSN13807501;15737721
Volume77Issue:22Pages:29905-29918
Contribution Rank4
Abstract

This paper proposes an ultrasound breast tumor CAD system based on BI-RADS features scoring and decision tree algorithm. Because of the difficulty of biopsy label collection, the proposed system adopts a few-shot learning method. The SVM classifier is employed to preliminarily mark the unlabeled cases firstly. Then these unlabeled cases with the pseudo labels are combined with the few real-labeled cases to train the decision tree. To test the performance of the proposed method, 1208 ultrasound breast images were collected, and three well-experienced clinicians and three interns evaluated these images according to the BI-RADS scoring scheme. All of the images are transformed into vectors such that the algorithm can process. The experimental results show that the system performance improves significantly with the help of pseudo-labeled data. Compared to the decision tree trained by the real-labeled cases only, when the number of real-labeled cases was 40, the accuracy, specificity, sensitivity of the proposed system were increased by 2.05%, 2.47% and 1.81%, respectively; the positive predictive value (PPV) and the negative predictive value (NVP) were increased by 1.29% and 3.05%, respectively. Meanwhile, the performance of the proposed method was the same as the method using sufficient samples. When the number of the labeled cases reached 100, the accuracy, specificity, sensitivity, PPV and NVP of the proposed method were 90.03%, 87.02%, 91.68%, 93.07%, and 85.03%, respectively. The results demonstrate that our method can efficiently distinguish the breast tumor although the labeled data is not sufficient. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

KeywordBreast Tumors Cad System Few-shot Learning Bi-rads Decision Tree
DOI10.1007/s11042-018-6026-1
Indexed BySCI ; EI
Language英语
WOS IDWOS:000451780800041
PublisherSpringer New York LLC
EI Accession Number20181805113100
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30848
Collection光学影像学习与分析中心
Corresponding AuthorHuang, Qinghua
Affiliation1.College of Information Engineering, Shenzhen University, Shenzhen; 518060, China;
2.School of Electronic and Information Engineering, South China University of Technology, Guangzhou; 510641, China;
3.School of Mechanical Engineering, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an; Shaanxi; 710072, China;
4.Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an; Shaanxi; 710119, China
Recommended Citation
GB/T 7714
Huang, Qinghua,Zhang, Fan,Li, Xuelong. Few-shot decision tree for diagnosis of ultrasound breast tumor using BI-RADS features[J]. Multimedia Tools and Applications,2018,77(22):29905-29918.
APA Huang, Qinghua,Zhang, Fan,&Li, Xuelong.(2018).Few-shot decision tree for diagnosis of ultrasound breast tumor using BI-RADS features.Multimedia Tools and Applications,77(22),29905-29918.
MLA Huang, Qinghua,et al."Few-shot decision tree for diagnosis of ultrasound breast tumor using BI-RADS features".Multimedia Tools and Applications 77.22(2018):29905-29918.
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