Improving Colonoscopy Polyp Detection Rate Using Semi-Supervised Learning

Colorectal cancer is one of the biggest health threats to humans and takes thousands of lives every year.Colonoscopy is the gold standard in clinical practice to inspect the intestinal wall,detect polyps and remove polypsin early stages,preventing polyps from becoming malignant and forming colorectal cancer instances.In recentyears,computer-aided polyp detection systems have been widely used in colonoscopies to improve the qualityof colonoscopy examination and increase the polyp detection rate.Currently,the most efficient computer-aidedsystems are built with machine learning methods.However,developing such a computer-aided detection systemrequires experienced doctors to label a large number of image data from colonoscopy videos,which is extremelytime-consuming,laborious and expensive.One possible solution is to adopt a semi-supervised learning,which canbuild a detection system on a dataset where part of its data is not necessary to be labeled.In this paper,on thebasis of state-of-the-art object detection method and semi-supervised learning technique,we design and implementa semi-supervised colonoscopy polyp detection system containing four main steps:running standard supervisedtraining with all labeled data;running inference on unlabeled data to obtain pseudo labels;applying a set ofstrong augmentation to both unlabeled data and pseudo label;combining labeled data,and unlabeled data withits pseudo labels to retrain the detector.The semi-supervised learning system is evaluated both on public datasetand our original private dataset and proves its effectiveness.Also,the inference speed of the semi-supervisedlearning system can meet the requirement of real-time operation.