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

Accurate image segmentation plays a crucial role in medical image analysis,yet it faces great challenges of various shapes, diverse sizes, and blurryboundaries. To address these difficulties, square kernel-based encoder-decoderarchitecture has been proposed and widely used, but its performance remainsstill unsatisfactory. To further cope with these challenges, we present a noveldouble-branch encoder architecture. Our architecture is inspired by twoobservations: 1) Since the discrimination of features learned via squareconvolutional kernels needs to be further improved, we propose to utilizenon-square vertical and horizontal convolutional kernels in the double-branchencoder, so features learned by the two branches can be expected to complementeach other. 2) Considering that spatial attention can help models to betterfocus on the target region in a large-sized image, we develop an attention lossto further emphasize the segmentation on small-sized targets. Together, theabove two schemes give rise to a novel double-branch encoder segmentationframework for medical image segmentation, namely Crosslink-Net. The experimentsvalidate the effectiveness of our model on four datasets. The code is releasedat https://github.com/Qianyu1226/Crosslink-Net.

Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions