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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