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

When a clinician refers a patient for an imaging exam, they include thereason (e.g. relevant patient history, suspected disease) in the scan request;this appears as the indication field in the radiology report. Theinterpretation and reporting of the image are substantially influenced by thisrequest text, steering the radiologist to focus on particular aspects of theimage. We use the indication field to drive better image classification, bytaking a transformer network which is unimodally pre-trained on text (BERT) andfine-tuning it for multimodal classification of a dual image-text input. Weevaluate the method on the MIMIC-CXR dataset, and present ablation studies toinvestigate the effect of the indication field on the classificationperformance. The experimental results show our approach achieves 87.8 averagemicro AUROC, outperforming the state-of-the-art methods for unimodal (84.4) andmultimodal (86.0) classification. Our code is available athttps://github.com/jacenkow/mmbt.

Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers