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Background: Colonoscopy remains the gold-standard screening for colorectalcancer. However, significant miss rates for polyps have been reported,particularly when there are multiple small adenomas. This presents anopportunity to leverage computer-aided systems to support clinicians and reducethe number of polyps missed.  Method: In this work we introduce the Focus U-Net, a novel dualattention-gated deep neural network, which combines efficient spatial andchannel-based attention into a single Focus Gate module to encourage selectivelearning of polyp features. The Focus U-Net further incorporates short-rangeskip connections and deep supervision. Furthermore, we introduce the HybridFocal loss, a new compound loss function based on the Focal loss and FocalTversky loss, to handle class-imbalanced image segmentation. For ourexperiments, we selected five public datasets containing images of polypsobtained during optical colonoscopy: CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB,ETIS-Larib PolypDB and EndoScene test set. To evaluate model performance, weuse the Dice similarity coefficient (DSC) and Intersection over Union (IoU)metrics.  Results: Our model achieves state-of-the-art results for both CVC-ClinicDBand Kvasir-SEG, with a mean DSC of 0.941 and 0.910, respectively. Whenevaluated on a combination of five public polyp datasets, our model similarlyachieves state-of-the-art results with a mean DSC of 0.878 and mean IoU of0.809, a 14% and 15% improvement over the previous state-of-the-art results of0.768 and 0.702, respectively.  Conclusions: This study shows the potential for deep learning to provide fastand accurate polyp segmentation results for use during colonoscopy. The FocusU-Net may be adapted for future use in newer non-invasive screening and morebroadly to other biomedical image segmentation tasks involving class imbalanceand requiring efficiency.

Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy