Real-Time Polyp Detection for Colonoscopy Video on CPU

Colorectal cancer(CRC), which is derived from polyps, is a common malignant tumor with a high incidence and mortality. Currently the colonoscopy is the most effective approach for early detection of the colorectal cancer. With the development of object detection, some polyp detection algorithms have achieved satisfied results. However, most of them need the support of hardware accelerators such as GPU, which is unsuitable for actual clinical environment due to its high power consumption and loud noise. To solve the problem, this paper proposes a solution to implement real-Time polyp detection for colonoscopy video on general-purpose computing platforms, i.e. CPU. Our solution incorporates several methods to improve efficiency of polyp detection. Firstly, by analyzing the similarity and difference between successive frames in colonoscopy videos, we use the abrupt shot detection method to reduce the unnecessary detection for frames that contain non-polyps; secondly, for the frames that need to be detected, we iterated a procedure of sparse training, pruning and knowledge distillation to search for compressed models. Our approach is evaluated with both public datasets and actual colonoscopy videos, and results show that our approach can process 35.28 frames per second on colonoscopy videos with satisfied precision on CPU.