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

This paper is responding to the MIA-COV19 challenge to classify COVID fromnon-COVID based on CT lung images. The COVID-19 virus has devastated the worldin the last eighteen months by infecting more than 182 million people andcausing over 3.9 million deaths. The overarching aim is to predict thediagnosis of the COVID-19 virus from chest radiographs, through the developmentof explainable vision transformer deep learning techniques, leading topopulation screening in a more rapid, accurate and transparent way. In thiscompetition, there are 5381 three-dimensional (3D) datasets in total, including1552 for training, 374 for evaluation and 3455 for testing. While most of thedata volumes are in axial view, there are a number of subjects' data are incoronal or sagittal views with 1 or 2 slices are in axial view. Hence, while 3Ddata based classification is investigated, in this competition, 2D imagesremains the main focus. Two deep learning methods are studied, which are visiontransformer (ViT) based on attention models and DenseNet that is built uponconventional convolutional neural network (CNN). Initial evaluation resultsbased on validation datasets whereby the ground truth is known indicate thatViT performs better than DenseNet with F1 scores being 0.76 and 0.72respectively. Codes are available at GitHub at<https://github/xiaohong1/COVID-ViT>.

COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models