Application of multiple empirical kernel mapping ensemble classifier based on self-paced learning in ultrasound-based computer-aided diagnosis for breast cancer.
10.7507/1001-5515.202002004
- Author:
Linlin WANG
1
;
Lu SHEN
1
;
Jun SHI
1
;
Xiaoyan FEI
1
;
Weijun ZHOU
2
;
Haoyu XU
3
;
Lizhuang LIU
3
Author Information
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.
- Publication Type:Journal Article
- Keywords:
breast cancer;
ensemble learning;
exclusivity regularized machine;
multiple empirical kernel mapping;
self-paced learning;
ultrasound imaging
- MeSH:
Algorithms;
Breast Neoplasms/diagnostic imaging*;
Computers;
Diagnosis, Computer-Assisted;
Humans;
Support Vector Machine;
Ultrasonography
- From:
Journal of Biomedical Engineering
2021;38(1):30-38
- CountryChina
- Language:Chinese
-
Abstract:
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.
