  Background: Colonoscopy remains the gold-standard screening for colorectal
cancer. 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, which combines efficient spatial and
channel-based attention into a single Focus Gate module to encourage selective
learning of polyp features. The Focus U-Net further incorporates short-range
skip connections and deep supervision. Furthermore, we introduce the Hybrid
Focal loss, a new compound loss function based on the Focal loss and Focal
Tversky loss, to handle class-imbalanced image segmentation. For our
experiments, we selected five public datasets containing images of polyps
obtained during optical colonoscopy: CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB,
ETIS-Larib PolypDB and EndoScene test set. To evaluate model performance, we
use the Dice similarity coefficient (DSC) and Intersection over Union (IoU)
metrics.
  Results: Our model achieves state-of-the-art results for both CVC-ClinicDB
and Kvasir-SEG, with a mean DSC of 0.941 and 0.910, respectively. When
evaluated on a combination of five public polyp datasets, our model similarly
achieves state-of-the-art results with a mean DSC of 0.878 and mean IoU of
0.809, a 14% and 15% improvement over the previous state-of-the-art results of
0.768 and 0.702, respectively.
  Conclusions: This study shows the potential for deep learning to provide fast
and accurate polyp segmentation results for use during colonoscopy. The Focus
U-Net may be adapted for future use in newer non-invasive screening and more
broadly to other biomedical image segmentation tasks involving class imbalance
and requiring efficiency.
