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

The identification and localization of diseases in medical images using deeplearning models have recently attracted significant interest. Existing methodsonly consider training the networks with each image independently and mostleverage an activation map for disease localization. In this paper, we proposean alternative approach that learns discriminative features among triplets ofimages and cyclically trains on region features to verify whether attentiveregions contain information indicative of a disease. Concretely, we adapt adistance learning framework for multi-label disease classification todifferentiate subtle disease features. Additionally, we feed back the featuresof the predicted class-specific regions to a separate classifier duringtraining to better verify the localized diseases. Our model can achievestate-of-the-art classification performance on the challenging ChestX-ray14dataset, and our ablation studies indicate that both distance learning andregion verification contribute to overall classification performance. Moreover,the distance learning and region verification modules can capture essentialinformation for better localization than baseline models without these modules.

Thoracic Disease Identification and Localization using Distance Learning and Region Verification