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Few-shot decision tree for diagnosis of ultrasound breast tumor using BI-RADS features

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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.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Nos. 61372007 and 61571193), Guangzhou Key Lab of Body Data Science (No.201605030011), Natural Science Foundation of Guangdong Province, China (Nos. 2017A030312006 and 2015A030313210), Fundamental Research Funds for the Central Universities (NO.2015ZM138), Project of Science and Technology Department of Guangdong province (2014A050503020, 2016A010101021, 2016A010101022 and 2016A010101023), Science and Technology Program of Guangzhou (no. 201704020134).

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Authors and Affiliations

  1. College of Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Qinghua Huang

  2. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China

    Qinghua Huang & Fan Zhang

  3. School of Mechanical Engineering, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

    Qinghua Huang

  4. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, Shaanxi, China

    Xuelong Li

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  1. Qinghua Huang
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  2. Fan Zhang
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Correspondence to Qinghua Huang.

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Huang, Q., Zhang, F. & Li, X. Few-shot decision tree for diagnosis of ultrasound breast tumor using BI-RADS features. Multimed Tools Appl 77, 29905–29918 (2018). https://doi.org/10.1007/s11042-018-6026-1

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  • DOI: https://doi.org/10.1007/s11042-018-6026-1

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