Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video).

[1]  P. Bossuyt,et al.  Polyp Miss Rate Determined by Tandem Colonoscopy: A Systematic Review , 2006, The American Journal of Gastroenterology.

[2]  Bjorn Winkens,et al.  Postcolonoscopy colorectal cancers are preventable: a population-based study , 2013, Gut.

[3]  Aymeric Histace,et al.  Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer , 2014, International Journal of Computer Assisted Radiology and Surgery.

[4]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[5]  Fernando Vilariño,et al.  WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians , 2015, Comput. Medical Imaging Graph..

[6]  Nima Tajbakhsh,et al.  Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information , 2016, IEEE Transactions on Medical Imaging.

[7]  G. Fernández-Esparrach,et al.  Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps , 2016, Endoscopy.

[8]  Michael Riegler,et al.  KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection , 2017, MMSys.

[9]  Kazuhiko Ohe,et al.  Regulatory Science on AI-based Medical Devices and Systems , 2018 .

[10]  K. Mori,et al.  Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy , 2018, Annals of Internal Medicine.

[11]  P. Baldi,et al.  Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. , 2018, Gastroenterology.

[12]  T. Berzin,et al.  Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy , 2018, Nature Biomedical Engineering.

[13]  Hayato Itoh,et al.  Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience. , 2018, Gastroenterology.

[14]  J. Sese,et al.  Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy , 2019, Scientific Reports.

[15]  T. Berzin,et al.  Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study , 2019, Gut.

[16]  Xiu-Li Zuo,et al.  Impact of real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with video). , 2020, Gastrointestinal endoscopy.

[17]  W. Zhou,et al.  Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. , 2020, The lancet. Gastroenterology & hepatology.

[18]  Peixi Liu,et al.  Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. , 2020, The lancet. Gastroenterology & hepatology.

[19]  Xuqiang Bian,et al.  Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy , 2019, Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association.