Abstract
Colon cancer is one of the leading causes of cancer-related deaths, while the CT colonoscopy has become the primary means of early detection of colon cancer. However, the majority of automatic detector of colon polyps in CT colonoscopy was got through offline training, which cannot be updated, when new samples were coming; simultaneously, polyp detection suffers from imbalanced data sets where negative samples (non-polyp) are dominant. Therefore, an online learning asymmetric approach was employed, which not only can update detector, but also can solve the problem of imbalanced data sets. Finally, experimental results show that the proposed algorithm can achieve good classification performance, and a shorter running time.
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