False-positive Reduction of Liver Tumor Detection Using Ensemble Learning Method

We proposed a novel ensemble learning method which can be applied to false-positive reduction of liver tumor detection. In many cases of the liver tumor detection, training data has some issues due to characteristics of liver tumors, and the conventional ensemble learning methods such as Bagging and AdaBoost tend to degrade sensitivity. The proposed method generates various weak classifiers based on adaptive sampling in order to enhance an ensemble effect against such issues, and can achieve accuracy satisfying requirements of liver tumor detection. We applied the method to 48 CT images and evaluated the accuracy. Results showed that the proposed method succeeded in reducing false positives greatly (from 3.96 to 1.10/image) while maintaining the required sensitivity.