基于海量内镜图像的结肠镜息肉检测
Colonoscopy polyp detection with massive endoscopic images
- HiThink Royalflush
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对原有结肠镜息肉检测框架鲁棒性不足的问题，通过优化锚框生成、更换骨干网络、加入注意力门控模块，在保持实时检测速度的同时实现了最先进的检测性能。
AI中文摘要：
我们改进了现有的端到端息肉检测模型，在检测速度成本极低的情况下，经不同数据集验证，其平均精度更优。我们此前开展的结肠镜息肉检测工作提供了一种高效的端到端方案，可减轻医生的检查负担。但后续实验发现，随着息肉拍摄条件发生变化，该框架的鲁棒性不如之前。在本研究中，我们对数据集进行了多项研究，找出了导致息肉检测任务中精度率较低的主要问题。我们采用了一种优化的锚框生成方法，以获得更优的锚框形状，同时使用更多框进行检测，因为我们认为这对小目标检测而言是必要的。我们采用了一种替代骨干网络，以弥补密集锚框回归带来的高额时间成本。通过引入注意力门控模块，我们的模型能够实现最先进的息肉检测性能，同时仍保持实时检测速度。
英文摘要：
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.
