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

Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a datascience competition standpoint, have limited utility in clinical use because oftheir narrow focus on diagnosing one specific disease. In real-world clinicaluse, multiple diseases need to be considered since they can co-exist in thesame patient. In this work, we demonstrate how federated learning (FL) can beused to make these toy CXR datasets from Kaggle clinically useful.Specifically, we train a single FL classification model (`global`) using twoseparate CXR datasets -- one annotated for presence of pneumonia and the otherfor presence of pneumothorax (two common and life-threatening conditions) --capable of diagnosing both. We compare the performance of the global FL modelwith models trained separately on both datasets (`baseline`) for two differentmodel architectures. On a standard, naive 3-layer CNN architecture, the globalFL model achieved AUROC of 0.84 and 0.81 for pneumonia and pneumothorax,respectively, compared to 0.85 and 0.82, respectively, for both baseline models(p>0.05). Similarly, on a pretrained DenseNet121 architecture, the global FLmodel achieved AUROC of 0.88 and 0.91 for pneumonia and pneumothorax,respectively, compared to 0.89 and 0.91, respectively, for both baseline models(p>0.05). Our results suggest that FL can be used to create global `meta`models to make toy datasets from Kaggle clinically useful, a step forwardtowards bridging the gap from bench to bedside.

From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning