AttResDU-Net: Medical Image Segmentation Using Attention-based Residual
  Double U-Net
Akib Mohammed Khan Alif Ashrafee Fahim Shahriar Khan Md. Bakhtiar Hasan Md. Hasanul Kabir
Manually inspecting polyps from a colonoscopy for colorectal cancer or
performing a biopsy on skin lesions for skin cancer are time-consuming,
laborious, and complex procedures. Automatic medical image segmentation aims to
expedite this diagnosis process. However, numerous challenges exist due to
significant variations in the appearance and sizes of objects with no distinct
boundaries. This paper proposes an attention-based residual Double U-Net
architecture (AttResDU-Net) that improves on the existing medical image
segmentation networks. Inspired by the Double U-Net, this architecture
incorporates attention gates on the skip connections and residual connections
in the convolutional blocks. The attention gates allow the model to retain more
relevant spatial information by suppressing irrelevant feature representation
from the down-sampling path for which the model learns to focus on target
regions of varying shapes and sizes. Moreover, the residual connections help to
train deeper models by ensuring better gradient flow. We conducted experiments
on three datasets: CVC Clinic-DB, ISIC 2018, and the 2018 Data Science Bowl
datasets and achieved Dice Coefficient scores of 94.35%, 91.68% and 92.45%
respectively. Our results suggest that AttResDU-Net can be facilitated as a
reliable method for automatic medical image segmentation in practice.