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Advances in Artificial Intelligence to Reduce Polyp Miss Rates during Colonoscopy

2021-05-16 16:10:32
Michael Yeung, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo
arXiv_CV
     
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

Abstract (translated)

URL

https://arxiv.org/abs/2105.07467

PDF

https://arxiv.org/pdf/2105.07467.pdf

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