TY - GEN
T1 - Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks
AU - Tajbakhsh, Nima
AU - Gurudu, Suryakanth R.
AU - Liang, Jianming
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/21
Y1 - 2015/7/21
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84944325843&partnerID=8YFLogxK
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U2 - 10.1109/ISBI.2015.7163821
DO - 10.1109/ISBI.2015.7163821
M3 - Conference contribution
AN - SCOPUS:84944325843
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 79
EP - 83
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Y2 - 16 April 2015 through 19 April 2015
ER -