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

Training robust deep learning (DL) systems for disease detection from medicalimages is challenging due to limited images covering different disease typesand severity. The problem is especially acute, where there is a severe classimbalance. We propose an active learning (AL) framework to select mostinformative samples for training our model using a Bayesian neural network.Informative samples are then used within a novel class aware generativeadversarial network (CAGAN) to generate realistic chest xray images for dataaugmentation by transferring characteristics from one class label to another.Experiments show our proposed AL framework is able to achieve state-of-the-artperformance by using about $35\%$ of the full dataset, thus saving significanttime and effort over conventional methods.

Informative sample generation using class aware generative adversarial networks for classification of chest Xrays