A fast and robust colorectal polyp detection framework in CT colonography was proposed. In order to speed the detection of polyp in CT colonography, a cascade-Adaboost framework was employed, and a lot of candidates were rejected quickly in the first stages of the cascade framework. To improve the performance of cascade-Adaboost, cascade indifference curve was explored to determine detection rate and false positive rate of cascade automatically. The experiments showed that the classifier could achieve an overall per-polyp sensitivity of 90% (for polyps' diameter 5 mm and greater), with false positives of 6 per volume on average.