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

Acute ischaemic stroke, caused by an interruption in blood flow to braintissue, is a leading cause of disability and mortality worldwide. The selectionof patients for the most optimal ischaemic stroke treatment is a crucial stepfor a successful outcome, as the effect of treatment highly depends on the timeto treatment. We propose a transformer-based multimodal network (TranSOP) for aclassification approach that employs clinical metadata and imaging information,acquired on hospital admission, to predict the functional outcome of stroketreatment based on the modified Rankin Scale (mRS). This includes a fusionmodule to efficiently combine 3D non-contrast computed tomography (NCCT)features and clinical information. In comparative experiments using unimodaland multimodal data on the MRCLEAN dataset, we achieve a state-of-the-art AUCscore of 0.85.

TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction