English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Manually inspecting polyps from a colonoscopy for colorectal cancer orperforming a biopsy on skin lesions for skin cancer are time-consuming,laborious, and complex procedures. Automatic medical image segmentation aims toexpedite this diagnosis process. However, numerous challenges exist due tosignificant variations in the appearance and sizes of objects with no distinctboundaries. This paper proposes an attention-based residual Double U-Netarchitecture (AttResDU-Net) that improves on the existing medical imagesegmentation networks. Inspired by the Double U-Net, this architectureincorporates attention gates on the skip connections and residual connectionsin the convolutional blocks. The attention gates allow the model to retain morerelevant spatial information by suppressing irrelevant feature representationfrom the down-sampling path for which the model learns to focus on targetregions of varying shapes and sizes. Moreover, the residual connections help totrain deeper models by ensuring better gradient flow. We conducted experimentson three datasets: CVC Clinic-DB, ISIC 2018, and the 2018 Data Science Bowldatasets and achieved Dice Coefficient scores of 94.35%, 91.68% and 92.45%respectively. Our results suggest that AttResDU-Net can be facilitated as areliable method for automatic medical image segmentation in practice.

AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net