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

In recent years, there has been increasing interest to incorporate attentioninto deep learning architectures for biomedical image segmentation. The modulardesign of attention mechanisms enables flexible integration into convolutionalneural network architectures, such as the U-Net. Whether attention isappropriate to use, what type of attention to use, and where in the network toincorporate attention modules, are all important considerations that arecurrently overlooked. In this paper, we investigate the role of the Focalparameter in modulating attention, revealing a link between attention in lossfunctions and networks. By incorporating a Focal distance penalty term, weextend the Unified Focal loss framework to include boundary-based losses.Furthermore, we develop a simple and interpretable, dataset and model-specificheuristic to integrate the Focal parameter into the Squeeze-and-Excitationblock and Attention Gate, achieving optimal performance with fewer number ofattention modules on three well-validated biomedical imaging datasets,suggesting judicious use of attention modules results in better performance andefficiency.

Focal Attention Networks: optimising attention for biomedical image segmentation