Abstract
Automatic polyp detection during colonoscopy screening test is desired to reduce polyp miss rate and thus lower patients’ risk of developing colorectal cancer. Previous works mainly focus on detection accuracy, however, real-time and robust polyp detection is as important to be adopted in clinical workflow. To maintain accuracy, speed and robustness for polyp detection at the same time, we propose a framework featuring two novel concepts: (1) decompose the task into detection and tracking steps to take advantage of both high resolution static images for accurate detection and the temporal information between frames for fast tracking and robustness. (2) run detector and tracker in two parallel threads asynchronously so that a heavy but accurate detector and a light tracker can efficiently work together. We also propose a robustness metric to evaluate performance in realistic clinical setting. Experiments demonstrated that our method outperformed the state-of-the-art results in terms of accuracy, robustness and speed.
Z. Zhang and H. Shang—Equal contribution.
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Acknowledgement
This work was founded by the Key Area Research and Development Program of Guangdong Province, China (No. 2018B010111001) and Science and Technology Program of Shenzhen, China (No. ZDSYS201802021814180).
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Zhang, Z. et al. (2020). Asynchronous in Parallel Detection and Tracking (AIPDT): Real-Time Robust Polyp Detection. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_69
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