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

The Coronavirus Disease 2019 (COVID-19) is affecting increasingly largenumber of people worldwide, posing significant stress to the health caresystems. Early and accurate diagnosis of COVID-19 is critical in screening ofinfected patients and breaking the person-to-person transmission. Chest X-ray(CXR) based computer-aided diagnosis of COVID-19 using deep learning becomes apromising solution to this end. However, the diverse and various radiographicfeatures of COVID-19 make it challenging, especially when considering each CXRscan typically only generates one single image. Data scarcity is another issuesince collecting large-scale medical CXR data set could be difficult atpresent. Therefore, how to extract more informative and relevant features fromthe limited samples available becomes essential. To address these issues,unlike traditional methods processing each CXR image from a single view, thispaper proposes triple-view convolutional neural networks for COVID-19 diagnosiswith CXR images. Specifically, the proposed networks extract individualfeatures from three views of each CXR image, i.e., the left lung view, theright lung view and the overall view, in three streams and then integrate themfor joint diagnosis. The proposed network structure respects the anatomicalstructure of human lungs and is well aligned with clinical diagnosis ofCOVID-19 in practice. In addition, the labeling of the views does not requireexperts' domain knowledge, which is needed by many existing methods. Theexperimental results show that the proposed method achieves state-of-the-artperformance, especially in the more challenging three class classificationtask, and admits wide generality and high flexibility.

Triple-view Convolutional Neural Networks for COVID-19 Diagnosis with Chest X-ray