Clustered Microcalcification is an important signal for breast cancer in the early stages. In this paper, we propose a Multiple Kernel SVM with Group Features (GF-SVM) to tackle problems associated with heterogeneous features of clustered microcalcification and normal breast tissues in suspicious regions. Specifically, different types of features such as being gradient, geometric and textural are grouped and modeled by different kernels, respectively. The prior knowledge from different resources is then combined into the framework of the multiple kernel SVM based classification scheme. Experimental results demonstrate that our classification scheme reduces the false positive rate significantly while maintaining the true positive rate.
展开▼
