NDLI: Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks

Content Provider	IEEE Xplore Digital Library
Author	Tajbakhsh, N. Gurudu, S.R. Jianming Liang
Copyright Year	2015
Description	Author affiliation: Dept. of Biomed. Inf., Arizona State Univ., Tempe, AZ, USA (Tajbakhsh, N.; Jianming Liang)
Abstract	Computer-aided polyp detection in colonoscopy videos has been the subject of research for over the past decade. However, despite significant advances, automatic polyp detection is still an unsolved problem. In this paper, we propose a new polyp detection method based on a unique 3-way image presentation and convolutional neural networks. Our method learns a variety of polyp features such as color, texture, shape, and temporal information in multiple scales, enabling a more accurate polyp localization. Given a polyp candidate, a set of convolution neural networks - each specialized in one type of features - are applied in the vicinity of the candidate and then their results are aggregated to either accept or reject the candidate. Our experimental results based on our collection of videos, which to our knowledge is the largest annotated polyp database, shows a remarkable performance improvement over the state-of-the-art, significantly reducing the number of false positives in nearly all operating points. In addition, we propose a new performance curve, demonstrating that our new method significantly decreases polyp detection latency, which is defined as the time from the first appearance of a polyp in the video to the time of its first detection by our method.
Starting Page	79
Ending Page	83
File Size	1960611
Page Count	5
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163821
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Colonoscopy Videos Image color analysis Shape Training Convolution Neural networks
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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