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.
|