Ultrasonic classification of breast tumors based on multi-instance learning
Jianhua Huang, Cong Hu, Yingtao Zhang, Jiafeng Liu, Xianglong Tang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Currently, locating the tumor ROI is the prerequisite of feature extraction. However, due to the low contrast and complex background of ultrasound images it is hard to obtain the accurate tumor ROI. Other organizations often been wrongly extracted as a tumor region, result in multi-ROI (non-tumor, tumor) in one image. As the result, the performance of tumor classification algorithms will be poor. In such case, ability to discriminate non-tumor and tumor area of classifier is of the most important. This paper proposed bag structure constructor on the basis of multi-ROI and multiple instance learning (MIL) classification algorithm is introduced to solve the above problem that has ability to discriminate non-tumor and tumor area to some extent. Experiments show that accuracy of the proposed method in such problems is 10% more than the traditional ultrasonic classification of breast tumor.