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

Pulmonary opacification is the inflammation in the lungs caused by manyrespiratory ailments, including the novel corona virus disease 2019 (COVID-19).Chest X-rays (CXRs) with such opacifications render regions of lungsimperceptible, making it difficult to perform automated image analysis on them.In this work, we focus on segmenting lungs from such abnormal CXRs as part of apipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat thehigh opacity regions as missing data and present a modified CNN-based imagesegmentation network that utilizes a deep generative model for data imputation.We train this model on normal CXRs with extensive data augmentation anddemonstrate the usefulness of this model to extend to cases with extremeabnormalities.

Lung Segmentation from Chest X-rays using Variational Data Imputation