Before revision:  NEW FINDINGS: We introduce a new deep neural network architecture, the Focus U-Net , which achieves state-of-the-art performance for polyp segmentationacross five public datasets containing images of polyps obtained during colonoscopy.
After revision:  However, significant miss rates for polyps have been reported, particularly when there are multiple small adenomas. This presents an opportunity to leverage computer-aided systems to support clinicians and reduce the number of polyps missed. Method: In this work we introduce the Focus U-Net, a novel dual attention-gated deep neural network architecture, the Focus U-Net , which achieves state-of-the-art performance for polyp segmentationacross five public datasets containing images of polyps obtained during colonoscopy.