论文标题
结肠镜检查息肉检测大量内窥镜图像
Colonoscopy polyp detection with massive endoscopic images
论文作者
论文摘要
我们改进了现有的端到端息肉检测模型，其平均精度得到了更好的平均精度，该模型由不同的数据集验证，并且在检测速度上具有微不足道的成本。我们先前在结肠镜检查中检测息肉的工作为减轻医生检查开销提供了有效的端到端解决方案。但是，我们后来的实验发现该框架不像以前那样坚固，不像息肉捕获的状况有所不同。在这项工作中，我们对数据集进行了几项研究，确定了在息肉检测任务中导致较低精度率的主要问题。我们使用了优化的锚生生成方法来获得更好的锚盒形状，并使用更多的盒子进行检测，因为我们认为这对于小对象检测是必不可少的。替代主链用于补偿密集的锚箱回归引入的大量时间成本。通过使用注意门模块，我们的模型可以实现最新的息肉检测性能，同时仍保持实时检测速度。
We improved an existing end-to-end polyp detection model with better average precision validated by different data sets with trivial cost on detection speed. Our previous work on detecting polyps within colonoscopy provided an efficient end-to-end solution to alleviate doctor's examination overhead. However, our later experiments found this framework is not as robust as before as the condition of polyp capturing varies. In this work, we conducted several studies on data set, identifying main issues that causes low precision rate in the task of polyp detection. We used an optimized anchor generation methods to get better anchor box shape and more boxes are used for detection as we believe this is necessary for small object detection. An alternative backbone is used to compensate the heavy time cost introduced by dense anchor box regression. With use of the attention gate module, our model can achieve state-of-the-art polyp detection performance while still maintain real-time detection speed.
