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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In the context of lung ultrasound, the detection of B-lines, which areindicative of interstitial lung disease and pulmonary edema, plays a pivotalrole in clinical diagnosis. Current methods still rely on visual inspection byexperts. Vision-based automatic B-line detection methods have been developed,but their performance has yet to improve in terms of both accuracy andcomputational speed. This paper presents a novel approach to posing B-linedetection as an inverse problem via deep unfolding of the Alternating DirectionMethod of Multipliers (ADMM). It tackles the challenges of data labelling andmodel training in lung ultrasound image analysis by harnessing the capabilitiesof deep neural networks and model-based methods. Our objective is tosubstantially enhance diagnostic accuracy while ensuring efficient real-timecapabilities. The results show that the proposed method runs more than 90 timesfaster than the traditional model-based method and achieves an F1 score that is10.6% higher.

DUBLINE: A Deep Unfolding Network for B-line Detection in Lung Ultrasound Images