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 extraction of labels from radiology text reports enables large-scaletraining of medical imaging models. Existing approaches to report labelingtypically rely either on sophisticated feature engineering based on medicaldomain knowledge or manual annotations by experts. In this work, we introduce aBERT-based approach to medical image report labeling that exploits both thescale of available rule-based systems and the quality of expert annotations. Wedemonstrate superior performance of a biomedically pretrained BERT model firsttrained on annotations of a rule-based labeler and then finetuned on a smallset of expert annotations augmented with automated backtranslation. We findthat our final model, CheXbert, is able to outperform the previous bestrules-based labeler with statistical significance, setting a new SOTA forreport labeling on one of the largest datasets of chest x-rays.

CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT