AFP-Net: Realtime Anchor-Free Polyp Detection in Colonoscopy
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Abstract (arXiv)
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 state-of-the-art performance with 99.36% precision and 96.44% recall.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2019.
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Citing papers per year: 2021: 1; 2022: 3.
4 papers.
- 2022Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challengecited by 27
- 2022Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unitcited by 4
- 2022Knowledge distillation with a class-aware loss for endoscopic disease detectioncited by 1
- 2021Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Studycited by 0
- 2022Knowledge distillation with a class-aware loss for endoscopic disease detection19 Jul 2022
- 2022Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge24 Feb 2022
- 2022Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit25 Jan 2022
- 2021Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study11 Jan 2021