Xia Wu1,2,3, Zhou Liu4, Meng Wang4, Zhe Ren1,2,3, Ya Ren4, Jie Wen4, Qian Yang4, Xin Liu1,2,3, Hairong Zheng1,2,3, and Na Zhang1,2,3
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Synopsis, ShenZhen, China, 2Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China, 3CAS key laboratory of health informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China, 4Department of Radiology, National Cancer Center/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, ShenZhen, China
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Synopsis, ShenZhen, China, 2Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China, 3CAS key laboratory of health informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, ShenZhen, China, 4Department of Radiology, National Cancer Center/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, ShenZhen, China
Combination of texture features and pharmacokinetic
parameters for a classifier can improve the accuracy of classification.
Table 1: The accuracy of breast cancer grading using three classifiers according
to different feature sets
Figure 1: Representative images of Grade Ⅰ-Ⅲ
breast cancer. The arrows point to the breast cancer lesions.