Abstract: Clustered microcalcification is an important signal for breast cancer in the early stages. However, computer aided detection of microcalcification is a challenge in the field of medical imaging. To improve the performance of the detection system, a large amount of lesion labeling is essential. Besides the difficulty on collecting samples itself, it also takes experts much time for manual labeling. Few state-of-the-art technictues take into account this problem. W first applied the techniques of active learning with SVM into this area to try to solve this problem. The basic conditions for the selected training set samples were proposed. I}he experiments on benchmark dataset show that our approach can reduce much works on labeling samples with holding the classification perfom}ance of the system of detecting interesting ROI regions.

Key words: Breast cancer,Computer aided detection, Active learning, Support vector machine