AFPNet: Realtime AnchorFree Polyp Detection in Colonoscopy Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. For colorectal cancer, the best screening test available is the colonoscopy. During a colonoscopic procedure, a tiny camera at the tip of the endoscope generates a video of the internal mucosa of the colon. The video data are displayed on a monitor for the physician to examine the lining of the entire colon and check for colorectal polyps. Detection and removal of colorectal polyps are associated with a reduction in mortality from colorectal cancer. However, the miss rate of polyp detection during colonoscopy procedure is often high even for very experienced physicians. The reason lies in the high variation of polyp in terms of shape, size, textural, color and illumination. Though challenging, with the great advances in object detection techniques, automated polyp detection still demonstrates a great potential in reducing the false negative rate while maintaining a high precision. In this paper, we propose a novel anchor free polyp detector that can localize polyps without using predefined anchor boxes. To further strengthen the model, we leverage a Context Enhancement Module and Cosine Ground truth Projection. Our approach can respond in real time while achieving stateoftheart performance with 99.36% precision and 96.44% recall.

An Efficient SpatialTemporal Polyp Detection Framework for Colonoscopy Video Recent computeraided polyp detection systems showed its effectiveness to decrease the polyp miss rate in colonoscopy operations, which is helpful to reduce colorectal cancer mortality. However, traditional polyp detection approaches suffer from the following drawbacks: low precision and sensitivity caused by the variance of polypu0027s appearance, and the system may not be able to detect polyps in real time due to the high computation complexity of the detection algorithms. To alleviate those problems, we introduce a realtime detection framework that incorporates spatial and temporal information extracted from colonoscopy videos. Our framework consists of the following three components: 1) we adopt Single Shot MultiBox Detector (SSD) to generate the proposal bounding boxes in each video frame. 2) Simultaneously, we compute optical flow from neighboring frames to extract temporal information and generate another group of polyp proposals with the temporal detection network. 3) At last, the final result is generated by a fusion module that connects the end of both streams. Experimental results on ETISLARIB dataset demonstrate that our proposed approach reaches the stateoftheart performance on polyp localization with realtime performance.