Application of multiple empirical kernel mapping ensemble classifier based on self-paced learning in ultrasound-based computer-aided diagnosis for breast cancer_Journal of Biomedical Engineering

Authors：

WANG Linlin ¹ , SHEN Lu ¹ , SHI Jun ¹ , FEI Xiaoyan ¹ ,  ZHOU Weijun ² , XU Haoyu ³ ,  LIU Lizhuang ³

1. Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, P.R. China;
2. Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, P.R. China;
3. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, P.R. China;

Corresponding author：

ZHOU Weijun, Email: wyky1985@163.com; LIU Lizhuang, Email: liulz@sari.ac.cn

Keywords：

breast cancer; ultrasound imaging; multiple empirical kernel mapping; self-paced learning; exclusivity regularized machine; ensemble learning

DOI：

10.7507/1001-5515.202002004

Video：

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Abstract Full text Figures/Tables Video References Cited by

Both feature representation and classifier performance are important factors that determine the performance of computer-aided diagnosis (CAD) systems. In order to improve the performance of ultrasound-based CAD for breast cancers, a novel multiple empirical kernel mapping (MEKM) exclusivity regularized machine (ERM) ensemble classifier algorithm based on self-paced learning (SPL) is proposed, which simultaneously promotes the performance of both feature representation and the classifier. The proposed algorithm first generates multiple groups of features by MEKM to enhance the ability of feature representation, which also work as the kernel transform in multiple support vector machines embedded in ERM. The SPL strategy is then adopted to adaptively select samples from easy to hard so as to gradually train the ERM classifier model with improved performance. This algorithm is verified on a B-mode ultrasound dataset and an elastography ultrasound dataset, respectively. The results show that the classification accuracy, sensitivity and specificity on B-mode ultrasound are (86.36±6.45)%, (88.15±7.12)%, and (84.52±9.38)%, respectively, and the classification accuracy, sensitivity and specificity on elastography ultrasound are (85.97±3.75)%, (85.93±6.09)%, and (86.03±5.88)%, respectively. It indicates that the proposed algorithm can effectively improve the performance of ultrasound-based CAD for breast cancers with the potential for application.

Citation： WANG Linlin, SHEN Lu, SHI Jun, FEI Xiaoyan, ZHOU Weijun, XU Haoyu, LIU Lizhuang. Application of multiple empirical kernel mapping ensemble classifier based on self-paced learning in ultrasound-based computer-aided diagnosis for breast cancer. Journal of Biomedical Engineering, 2021, 38(1): 30-38. doi: 10.7507/1001-5515.202002004 Copy

1.	Mills P, Saverymuttu S, Fallowfield M, et al. Ultrasound in the diagnosis of granulomatous liver disease. Clin Radiol, 1990, 41(2): 113-115.
2.	Guo R, Lu G, Qin B, et al. Ultrasound imaging technologies for breast cancer detection and management: a review. Ultrasound Med Biol, 2018, 44(1): 37-70.
3.	Shi Jun, Zheng Xiao, Li Yan, et al. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease. IEEE J Biomed Health Inform, 2018, 22(1): 173-183.
4.	Shi Jun, Xue Zeyu, Dai Yakang, et al. Cascaded Multi-Column RVFL+ classifier for single-modal neuroimaging-based diagnosis of Parkinson's disease. IEEE Trans Biomed Eng, 2019, 66(8): 2362-2371.
5.	陈诗慧, 刘维湘, 秦璟, 等. 基于深度学习和医学图像的癌症计算机辅助诊断研究进展. 生物医学工程学杂志, 2017, 34(2): 314-319.
6.	Zhou Shichong, Shi Jun, Zhu Jie, et al. Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image. Biomed Signal Process Control, 2013, 8(6): 688-696.
7.	Shi Jun, Zhou Shichong, Liu Xiao, et al. Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing, 2016, 194: 87-94.
8.	El-Dahshan E A, Mohsen H M, Revett K, et al. Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl, 2014, 41(11): 5526-5545.
9.	Hearst M A, Dumais S T, Osuna E, et al. Support vector machines. IEEE Intelligent Systems and Their Applications, 1998, 13(4): 18-28.
10.	Li Xuchun, Wang Lei, Sung E. AdaBoost with SVM-based component classifiers. Eng Appl Artif Intell, 2008, 21(5): 785-795.
11.	Guo X, Wang X, Ling H, et al. Exclusivity regularized machine: a new ensemble SVM classifier//Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia: AAAI Press, 2017: 1739-1745.
12.	Pillonetto G, Dinuzzo F, Chen Tianshi, et al. Kernel methods in system identification, machine learning and function estimation: a survey. Automatica, 2014, 50(3): 657-682.
13.	Nazarpour A, Adibi P. Two-stage multiple kernel learning for supervised dimensionality reduction. Pattern Recognit, 2015, 48(5): 1854-1862.
14.	Xiong H, Swamy M N, Ahmad M. Optimizing the kernel in the empirical feature space. IEEE Trans Neural Netw, 2005, 16(2): 460-474.
15.	Wang Zhe, Jie Wenbo, Chen Songcan, et al. Random projection ensemble learning with multiple empirical kernels. Knowl Based Syst, 2013, 37(2): 388-393.
16.	Kumar M P, Packer B, Koller D. Self-paced learning for latent variable models//Advances in Neural Information Processing Systems. Red Hook, USA: Curran Associates Inc, 2010: 1189-1197.
17.	Bengio Y, Louradour J, Collobert R, et al. Curriculum learning//Proceedings of the Twenty-sixth Annual International Conference on Machine Learning, Montreal Quebec, Canada: ACM, 2009: 41-48.
18.	Pi T, Li X, Zhang Z, et al. Self-paced boost learning for classification//International Joint Conferences on Artificial Intelligence. New York, USA: AAAI Press, 2016: 1932-1938.
19.	Fan Qi, Wang Zhe, Zha Hongyuan, et al. MREKLM: a fast multiple empirical kernel learning machine. Pattern Recognit, 2017, 61: 197-209.
20.	Yang J, Wu X, Liang J, et al. Self-Paced balance learning for clinical skin disease recognition. IEEE Trans Neural Netw Learn Syst, 2020, 31(8): 2832-2846.
21.	Yang X, Tridandapani S, Beitler J J, et al. Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity. Medical Physics, 2012, 39(9): 5732-5739.
22.	Zhang Qi, Xiao Yang, Suo Jingfeng, et al. Sonoelastomics for breast tumor classification: a radiomics approach with clustering-based feature selection on sonoelastography. Ultrasound in Medicine and Biology, 2017, 43(5): 1058-1069.
23.	Chen L, Hagenah J, Mertins A. Feature analysis for Parkinson's disease detection based on transcranial sonography image//International Conference on Medical Image Computing and Computer-assisted Intervention, Berlin, Heidelberg: Springer, 2012: 272-279.
24.	Stehman S V. Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ, 1997, 62(1): 77-89.

1. Mills P, Saverymuttu S, Fallowfield M, et al. Ultrasound in the diagnosis of granulomatous liver disease. Clin Radiol, 1990, 41(2): 113-115.
2. Guo R, Lu G, Qin B, et al. Ultrasound imaging technologies for breast cancer detection and management: a review. Ultrasound Med Biol, 2018, 44(1): 37-70.
3. Shi Jun, Zheng Xiao, Li Yan, et al. Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease. IEEE J Biomed Health Inform, 2018, 22(1): 173-183.
4. Shi Jun, Xue Zeyu, Dai Yakang, et al. Cascaded Multi-Column RVFL+ classifier for single-modal neuroimaging-based diagnosis of Parkinson's disease. IEEE Trans Biomed Eng, 2019, 66(8): 2362-2371.
5. 陈诗慧, 刘维湘, 秦璟, 等. 基于深度学习和医学图像的癌症计算机辅助诊断研究进展. 生物医学工程学杂志, 2017, 34(2): 314-319.
6. Zhou Shichong, Shi Jun, Zhu Jie, et al. Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image. Biomed Signal Process Control, 2013, 8(6): 688-696.
7. Shi Jun, Zhou Shichong, Liu Xiao, et al. Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset. Neurocomputing, 2016, 194: 87-94.
8. El-Dahshan E A, Mohsen H M, Revett K, et al. Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl, 2014, 41(11): 5526-5545.
9. Hearst M A, Dumais S T, Osuna E, et al. Support vector machines. IEEE Intelligent Systems and Their Applications, 1998, 13(4): 18-28.
10. Li Xuchun, Wang Lei, Sung E. AdaBoost with SVM-based component classifiers. Eng Appl Artif Intell, 2008, 21(5): 785-795.
11. Guo X, Wang X, Ling H, et al. Exclusivity regularized machine: a new ensemble SVM classifier//Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia: AAAI Press, 2017: 1739-1745.
12. Pillonetto G, Dinuzzo F, Chen Tianshi, et al. Kernel methods in system identification, machine learning and function estimation: a survey. Automatica, 2014, 50(3): 657-682.
13. Nazarpour A, Adibi P. Two-stage multiple kernel learning for supervised dimensionality reduction. Pattern Recognit, 2015, 48(5): 1854-1862.
14. Xiong H, Swamy M N, Ahmad M. Optimizing the kernel in the empirical feature space. IEEE Trans Neural Netw, 2005, 16(2): 460-474.
15. Wang Zhe, Jie Wenbo, Chen Songcan, et al. Random projection ensemble learning with multiple empirical kernels. Knowl Based Syst, 2013, 37(2): 388-393.
16. Kumar M P, Packer B, Koller D. Self-paced learning for latent variable models//Advances in Neural Information Processing Systems. Red Hook, USA: Curran Associates Inc, 2010: 1189-1197.
17. Bengio Y, Louradour J, Collobert R, et al. Curriculum learning//Proceedings of the Twenty-sixth Annual International Conference on Machine Learning, Montreal Quebec, Canada: ACM, 2009: 41-48.
18. Pi T, Li X, Zhang Z, et al. Self-paced boost learning for classification//International Joint Conferences on Artificial Intelligence. New York, USA: AAAI Press, 2016: 1932-1938.
19. Fan Qi, Wang Zhe, Zha Hongyuan, et al. MREKLM: a fast multiple empirical kernel learning machine. Pattern Recognit, 2017, 61: 197-209.
20. Yang J, Wu X, Liang J, et al. Self-Paced balance learning for clinical skin disease recognition. IEEE Trans Neural Netw Learn Syst, 2020, 31(8): 2832-2846.
21. Yang X, Tridandapani S, Beitler J J, et al. Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity. Medical Physics, 2012, 39(9): 5732-5739.
22. Zhang Qi, Xiao Yang, Suo Jingfeng, et al. Sonoelastomics for breast tumor classification: a radiomics approach with clustering-based feature selection on sonoelastography. Ultrasound in Medicine and Biology, 2017, 43(5): 1058-1069.
23. Chen L, Hagenah J, Mertins A. Feature analysis for Parkinson's disease detection based on transcranial sonography image//International Conference on Medical Image Computing and Computer-assisted Intervention, Berlin, Heidelberg: Springer, 2012: 272-279.
24. Stehman S V. Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ, 1997, 62(1): 77-89.

Journal of Biomedical Engineering

Application of multiple empirical kernel mapping ensemble classifier based on self-paced learning in ultrasound-based computer-aided diagnosis for breast cancer

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