Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/136486
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Type: Conference paper
Title: In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos
Author: Butler, D.
Zhang, Y.
Chen, T.
Shin, S.H.
Singh, R.
Carneiro, G.
Citation: Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2022, vol.2022
Publisher: IEEE
Publisher Place: Online
Issue Date: 2022
Series/Report no.: IEEE International Symposium on Biomedical Imaging
ISBN: 9781665429245
ISSN: 1945-7928
1945-8452
Conference Name: IEEE International Symposium on Biomedical Imaging (ISBI) (28 Mar 2022 - 31 Mar 2022 : virtual online, Kolkata, India)
Statement of
Responsibility: 
David Butler, Yuan Zhang, Tim Chen, Seon Ho Shin, Rajvinder Singh, Gustavo Carneiro
Abstract: Real-time and robust automatic detection of polyps from colonoscopy videos are essential tasks to help improve the performance of doctors during this exam. The current focus of the field is on the development of accurate but inefficient detectors that will not enable a real-time application. We advocate that the field should instead focus on the development of simple and efficient detectors that can be combined with effective trackers to allow the implementation of real-time polyp detectors. In this paper, we propose a Kalman filtering tracker that can work together with powerful, but efficient detectors, enabling the implementation of real-time polyp detectors. In particular, we show that the combination of our Kalman filtering with the detector PP-YOLO shows state-of-the-art (SOTA) detection accuracy and real-time processing. More specifically, our approach has SOTA results on the CVC-ClinicDB dataset, with a recall of 0.740, precision of 0.869, F 1 score of 0.799, an average precision (AP) of 0.837, and can run in real time (i.e., 30 frames per second). We also evaluate our method on a subset of the Hyper-Kvasir annotated by our clinical collaborators, resulting in SOTA results, with a recall of 0.956, precision of 0.875, F 1 score of 0.914, AP of 0.952, and can run in real time 1.
Rights: ©2022 IEEE
DOI: 10.1109/ISBI52829.2022.9761436
Grant ID: http://purl.org/au-research/grants/arc/DP180103232
http://purl.org/au-research/grants/arc/FT190100525
Published version: https://ieeexplore.ieee.org/xpl/conhome/9761376/proceeding
Appears in Collections:Computer Science publications
Medicine publications

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