作者
Jorge Bernal, Nima Tajkbaksh, Francisco Javier Sanchez, Bogdan J Matuszewski, Hao Chen, Lequan Yu, Quentin Angermann, Olivier Romain, Bjørn Rustad, Ilangko Balasingham, Konstantin Pogorelov, Sungbin Choi, Quentin Debard, Lena Maier-Hein, Stefanie Speidel, Danail Stoyanov, Patrick Brandao, Henry Cordova, Cristina Sanchez-Montes, Suryakanth R Gurudu, Gloria Fernandez-Esparrach, Xavier Dray, Jianming Liang, Aymeric Histace
发表日期
2017/2/2
期刊
IEEE transactions on medical imaging
卷号
36
期号
6
页码范围
1231-1249
出版商
IEEE
简介
Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences …
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