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

Due to the scarcity and specific imaging characteristics in medical images,light-weighting Vision Transformers (ViTs) for efficient medical imagesegmentation is a significant challenge, and current studies have not yet paidattention to this issue. This work revisits the relationship between CNNs andTransformers in lightweight universal networks for medical image segmentation,aiming to integrate the advantages of both worlds at the infrastructure designlevel. In order to leverage the inductive bias inherent in CNNs, we abstract aTransformer-like lightweight CNNs block (ConvUtr) as the patch embeddings ofViTs, feeding Transformer with denoised, non-redundant and highly condensedsemantic information. Moreover, an adaptive Local-Global-Local (LGL) block isintroduced to facilitate efficient local-to-global information flow exchange,maximizing Transformer's global context information extraction capabilities.Finally, we build an efficient medical image segmentation model (MobileUtr)based on CNN and Transformer. Extensive experiments on five public medicalimage datasets with three different modalities demonstrate the superiority ofMobileUtr over the state-of-the-art methods, while boasting lighter weights andlower computational cost. Code is available athttps://github.com/FengheTan9/MobileUtr.

MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation