  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.
