论文标题
捍卫卡尔曼过滤的息肉跟踪，从结肠镜检查视频
In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos
论文作者
论文摘要
实时和强大的自动检测结肠镜检查视频是帮助改善本考试期间医生表现的重要任务。该领域的当前重点是开发准确但效率低下的检测器，这些检测器将无法实时应用。我们主张该领域应该专注于简单有效的检测器的开发，这些检测器与有效的跟踪器结合起来，以实现实时息肉检测器。在本文中，我们提出了一个Kalman过滤跟踪器，该跟踪器可以与功能强大但有效的检测器一起使用，从而实现实时息肉检测器。特别是，我们表明，我们的卡尔曼过滤与检测器PP-yolo的组合显示了最新的（SOTA）检测准确性和实时处理。更具体地说，我们的方法在CVC-ClinicDB数据集上具有SOTA结果，召回0.740，精度为0.869，$ F_1 $得分为0.799，平均精度（AP）为0.837，可以实时运行（即每秒30帧每秒）。我们还评估了我们的临床合作者注释的超级KVASIR的子集的方法，从而产生了SOTA结果，召回0.956，精度为0.875，$ F_1 $得分为0.914，AP为0.952，可以实时运行。
Real-time and robust automatic detection of polyps from colonoscopy videos are essential tasks to help improve the performance of doctors during this exam. The current focus of the field is on the development of accurate but inefficient detectors that will not enable a real-time application. We advocate that the field should instead focus on the development of simple and efficient detectors that an be combined with effective trackers to allow the implementation of real-time polyp detectors. In this paper, we propose a Kalman filtering tracker that can work together with powerful, but efficient detectors, enabling the implementation of real-time polyp detectors. In particular, we show that the combination of our Kalman filtering with the detector PP-YOLO shows state-of-the-art (SOTA) detection accuracy and real-time processing. More specifically, our approach has SOTA results on the CVC-ClinicDB dataset, with a recall of 0.740, precision of 0.869, $F_1$ score of 0.799, an average precision (AP) of 0.837, and can run in real time (i.e., 30 frames per second). We also evaluate our method on a subset of the Hyper-Kvasir annotated by our clinical collaborators, resulting in SOTA results, with a recall of 0.956, precision of 0.875, $F_1$ score of 0.914, AP of 0.952, and can run in real time.
