1. Volume 155, Issue 4, Pages 1069-1078.e8 (October 2018)
Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy 
Gregor Urban, Priyam Tripathi, Talal Alkayali, Mohit Mittal, Farid Jalali, William Karnes, Pierre Baldi 
Gastroenterology 
Volume 155, Issue 4, Pages e8 (October 2018)
DOI: /j.gastro
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2. Gastroenterology 2018 155, 1069-1078. e8DOI: (10. 1053/j. gastro. 2018
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3. Figure 1
Examples of dataset. (Top row) Images containing a polyp with a superimposed bounding box. (Bottom row) Non-polyp images. Three pictures on the left were taken using NBI and 3 pictures on the right include tools (eg, biopsy forceps, cuff devices, etc) that are commonly used in screening colonoscopy procedures.
Gastroenterology  , e8DOI: ( /j.gastro )
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4. Figure 2
Representative frame shots of CNN-overlaid colonoscopy videos. Presence of a green box indicates that a polyp is detected with greater than 95% confidence by our CNN polyp localization model; the location and size of the box are predictions of the CNN model. Expert confidence that the box contained a true polyp is shown in the upper left of the images (video collages of CNN localization predictions available at:
Gastroenterology  , e8DOI: ( /j.gastro )
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5. Supplementary Figure 1
Effect of the size of the filtering window on the sensitivity and specificity of CNN predictions using a fixed threshold of 0.4.
Gastroenterology  , e8DOI: ( /j.gastro )
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6. Supplementary Figure 2
Receiver operator characteristic curve for all 5 CNN architectures trained on subsets of the 8641 colonoscopy images. Results obtained on the test splits of the 8641 colonoscopy images.
Gastroenterology  , e8DOI: ( /j.gastro )
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