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.

翻译：结直肠癌（CRC）常由最初良性的息肉发展而来，是导致全球癌症相关死亡的重要因素。通过结肠镜检查及早准确检测这些息肉对于CRC预防至关重要。然而，传统结肠镜检查方法高度依赖操作者经验，导致息肉检出率欠佳。此外，公共数据库中的息肉尺寸和形状多样性有限。为增强息肉检测的可用数据，我们提出Consisaug——一种创新且有效的基于深度学习的增强数据方法。我们利用以下约束条件：当图像被翻转时，类别标签应保持不变，边界框应保持一致性。我们在五个公共息肉数据集及三个骨干网络上实施Consisaug，结果表明了该方法的有效性。