@Article{JorgeBernal2017,
author="Jorge Bernal
and Nima Tajkbaksh
and F. Javier Sanchez
and Bogdan J. Matuszewski
and Hao Chen
and Lequan Yu
and Quentin Angermann
and Olivier Romain
and Bjorn Rustad
and Ilangko Balasingham
and Konstantin Pogorelov
and Sungbin Choi
and Quentin Debard
and Lena Maier Hein
and Stefanie Speidel
and Danail Stoyanov
and Patrick Brandao
and Henry Cordova
and Cristina Sanchez Montes
and Suryakanth R. Gurudu
and Gloria Fernandez Esparrach
and Xavier Dray
and Jianming Liang
and Aymeric Histace",
title="Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge",
journal="IEEE Transactions on Medical Imaging",
year="2017",
volume="36",
number="6",
pages="1231--1249",
optkeywords="Endoscopic vision",
optkeywords="Polyp Detection",
optkeywords="Handcrafted features",
optkeywords="Machine Learning",
optkeywords="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 lackof 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 AssistedIntervention (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.",
optnote="MV; 600.096; 600.075",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2949), last updated on Thu, 16 Feb 2023 11:58:52 +0100",
doi="10.1109/TMI.2017.2664042",
file=":http://refbase.cvc.uab.es/files/BTS2017.pdf:PDF"
}