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

Transesophageal echocardiography (TEE) is widely used to detect left atrium(LA)/left atrial appendage (LAA) thrombi. In this paper, the local binarypattern variance (LBPV) features are extracted from region of interest (ROI).And the dynamic features are formed by using the information of its neighborframes in the sequence. The sequence is viewed as a bag, and the images in thesequence are considered as the instances. Multiple-instance learning (MIL)method is employed to solve the LAA thrombi detection. The experimental resultsshow that the proposed method can achieve better performance than that by usingother methods.

An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography