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Polyp Detection in Colonoscopy Videos by Bootstrapping Via Temporal Consistency

Computer-aided polyp detection during colonoscopy is beneficial to reduce the risk of colorectal cancers. Deep learning techniques have made significant process in natural object detection. However, when applying those fully supervised methods to polyp detection, the performance is greatly depressed by the deficiency of labeled data. In this paper, we propose a novel bootstrapping method for polyp detection in colonoscopy videos by augmenting training data with temporal consistency. For a detection network that is trained on a small set of annotated polyp images, we fine-tune it with new samples selected from the test video itself, in order to more effectively represent the polyp morphology of current video. A strategy of selecting new samples is proposed by considering temporal consistency in the test video. Evaluated on 11954 endoscopic frames of the CVC-ClinicVideoDB dataset, our method yields great improvement on polyp detection for several detection networks, and achieves state-of-the-art performance on the benchmark dataset.