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Author Jorge Bernal; Nima Tajkbaksh; F. Javier Sanchez; Bogdan J. Matuszewski; Hao Chen; Lequan Yu; Quentin Angermann; Olivier Romain; Bjorn 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 edit   pdf
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  Title Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge Type Journal Article
  Year 2017 Publication IEEE Transactions on Medical Imaging Abbreviated Journal (up) TMI  
  Volume 36 Issue 6 Pages 1231 - 1249  
  Keywords Endoscopic vision; Polyp Detection; Handcrafted features; Machine Learning; Validation Framework  
  Abstract Colonoscopy is the gold standard for colon cancer screening though still some polyps are 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 subchallenge, 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 between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.
 
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  Notes MV; 600.096; 600.075 Approved no  
  Call Number Admin @ si @ BTS2017 Serial 2949  
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