2021
DOI: 10.1109/access.2021.3063716
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Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

Abstract: Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer-vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks… Show more

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Cited by 251 publications
(101 citation statements)
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“…The first reason is that the research questions were different. We focused on the classification according to the histopathological ground truths, however, several groups dedicated their efforts to detect polyps on the images (39,40). Many studies have been conducted on computer-aided detection to decrease the missing rate of colon polyps.…”
Section: Discussion Discussionmentioning
confidence: 99%
“…The first reason is that the research questions were different. We focused on the classification according to the histopathological ground truths, however, several groups dedicated their efforts to detect polyps on the images (39,40). Many studies have been conducted on computer-aided detection to decrease the missing rate of colon polyps.…”
Section: Discussion Discussionmentioning
confidence: 99%
“…It would be interesting for future work to investigate more recent models like ColonSegNet [26], NanoNet [27] RESUNET++ [28], and DoubleUNet [24] as well as larger image sizes, as our experiments were limited to image sizes of 128x128 and the standard U-NET and Pix2Pix models. There are also other unsupervised models related to Pix2Pix which may be of interest.…”
Section: Discussionmentioning
confidence: 99%
“…Priotising efficiency over performance, PolypSegNet introduces the depth dilated inception module, enabling efficient feature extraction across a range of receptive field sizes [ 28 ]. Similarly, ColonSegNet is a light-weight network that includes residual connections and channel attention to achieve real-time polyp segmentation [ 29 ]. PraNet uses a two-step process that involves initial localisation of the polyp area, followed by progressive refining of the polyp boundary, resembling the method by which humans identify polyps [ 30 ].…”
Section: Related Workmentioning
confidence: 99%