Polyp detection on video colonoscopy using a hybrid 2D/3D CNN

Author:

González-Bueno Puyal JuanaORCID,Brandao Patrick,Ahmad Omer F.,Bhatia Kanwal K.,Toth Daniel,Kader RawenORCID,Lovat Laurence,Mountney Peter,Stoyanov Danail

Funder

Horizon 2020

Wellcome Trust

Engineering and Physical Sciences Research Council

Royal Academy of Engineering

European Space Agency

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference46 articles.

1. Establishing key research questions for the implementation of artificial intelligence in colonoscopy-a modified Delphi method;Ahmad;Endoscopy,2020

2. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions;Ahmad;Lancet Gastroenterol. Hepatol.,2019

3. Barriers and pitfalls for artificial intelligence in gastroenterology: ethical and regulatory issues;Ahmad;Tech. Gastrointest. Endosc.,2019

4. Unsupervised learning of endoscopy video frames’ correspondences from global and local transformation;Armin,2018

5. Comparative validation of polyp detection methods in video colonoscopy: results from the MICCAI 2015 endoscopic vision challenge;Bernal;IEEE Trans. Med. Imaging,2017

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