  Colorectal cancer (CRC), which frequently originates from initially benign
polyps, remains a significant contributor to global cancer-related mortality.
Early and accurate detection of these polyps via colonoscopy is crucial for CRC
prevention. However, traditional colonoscopy methods depend heavily on the
operator's experience, leading to suboptimal polyp detection rates. Besides,
the public database are limited in polyp size and shape diversity. To enhance
the available data for polyp detection, we introduce Consisaug, an innovative
and effective methodology to augment data that leverages deep learning. We
utilize the constraint that when the image is flipped the class label should be
equal and the bonding boxes should be consistent. We implement our Consisaug on
five public polyp datasets and at three backbones, and the results show the
effectiveness of our method.
