Advances in Artificial Intelligence to Reduce Polyp Miss Rates during
Colonoscopy
- URL: http://arxiv.org/abs/2105.07467v1
- Date: Sun, 16 May 2021 16:10:32 GMT
- Title: Advances in Artificial Intelligence to Reduce Polyp Miss Rates during
Colonoscopy
- Authors: Michael Yeung, Evis Sala, Carola-Bibiane Sch\"onlieb, Leonardo Rundo
- Abstract summary: We introduce a new deep neural network architecture, which achieves state-of-the-art performance for polyp segmentation.
Our algorithm could be integrated into colonoscopy practice and assist gastroenterologists by reducing the number of polyps missed.
- Score: 0.7619404259039283
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: BACKGROUND AND CONTEXT: Artificial intelligence has the potential to aid
gastroenterologists by reducing polyp miss detection rates during colonoscopy
screening for colorectal cancer.
NEW FINDINGS: We introduce a new deep neural network architecture, the Focus
U-Net, which achieves state-of-the-art performance for polyp segmentation
across five public datasets containing images of polyps obtained during
colonoscopy.
LIMITATIONS: The model has been validated on images taken during colonoscopy
but requires validation on live video data to ensure generalisability.
IMPACT: Once validated on live video data, our polyp segmentation algorithm
could be integrated into colonoscopy practice and assist gastroenterologists by
reducing the number of polyps missed
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