  Automatic detection of polyps is challenging because different polyps vary
greatly, while the changes between polyps and their analogues are small. The
state-of-the-art methods are based on convolutional neural networks (CNNs).
However, they may fail due to lack of training data, resulting in high rates of
missed detection and false positives (FPs). In order to solve these problems,
our method combines the two-dimensional (2-D) CNN-based real-time object
detector network with spatiotemporal information. Firstly, we use a 2-D
detector network to detect static images and frames, and based on the detector
network, we propose two feature enhancement modules-the FP Relearning Module
(FPRM) to make the detector network learning more about the features of FPs for
higher precision, and the Image Style Transfer Module (ISTM) to enhance the
features of polyps for sensitivity improvement. In video detection, we
integrate spatiotemporal information, which uses Structural Similarity (SSIM)
to measure the similarity between video frames. Finally, we propose the
Inter-frame Similarity Correlation Unit (ISCU) to combine the results obtained
by the detector network and frame similarity to make the final decision. We
verify our method on both private databases and publicly available databases.
Experimental results show that these modules and units provide a performance
improvement compared with the baseline method. Comparison with the
state-of-the-art methods shows that the proposed method outperforms the
existing ones which can meet real-time constraints. It's demonstrated that our
method provides a performance improvement in sensitivity, precision and
specificity, and has great potential to be applied in clinical colonoscopy.
