  Colorectal polyps are abnormal tissues growing on the intima of the colon or
rectum with a high risk of developing into colorectal cancer, the third leading
cause of cancer death worldwide. Early detection and removal of colon polyps
via colonoscopy have proved to be an effective approach to prevent colorectal
cancer. Recently, various CNN-based computer-aided systems have been developed
to help physicians detect polyps. However, these systems do not perform well in
real-world colonoscopy operations due to the significant difference between
images in a real colonoscopy and those in the public datasets. Unlike the
well-chosen clear images with obvious polyps in the public datasets, images
from a colonoscopy are often blurry and contain various artifacts such as
fluid, debris, bubbles, reflection, specularity, contrast, saturation, and
medical instruments, with a wide variety of polyps of different sizes, shapes,
and textures. All these factors pose a significant challenge to effective polyp
detection in a colonoscopy. To this end, we collect a private dataset that
contains 7,313 images from 224 complete colonoscopy procedures. This dataset
represents realistic operation scenarios and thus can be used to better train
the models and evaluate a system's performance in practice. We propose an
integrated system architecture to address the unique challenges for polyp
detection. Extensive experiments results show that our system can effectively
detect polyps in a colonoscopy with excellent performance in real time.
