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

We present a weakly supervised deep learning model for classifying thoracicdiseases and identifying abnormalities in chest radiography. In this work,instead of learning from medical imaging data with region-level annotations,our model was merely trained on imaging data with image-level labels toclassify diseases, and is able to identify abnormal image regionssimultaneously. Our model consists of a customized pooling structure and anadaptive DenseNet front-end, which can effectively recognize possible diseasefeatures for classification and localization tasks. Our method has beenvalidated on the publicly available ChestX-ray14 dataset. Experimental resultshave demonstrated that our classification and localization predictionperformance achieved significant improvement over the previous models on theChestX-ray14 dataset. In summary, our network can produce accurate diseaseclassification and localization, which can potentially support clinicaldecisions.

A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities