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

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Acute ischaemic stroke, caused by an interruption in blood flow to brain tissue, is a leading cause of disability and mortality worldwide. The selection of patients for the most optimal ischaemic stroke treatment is a crucial step for a successful outcome, as the effect of treatment highly depends on the time to treatment. We propose a transformer-based multimodal network (TranSOP) for a classification approach that employs clinical metadata and imaging information, acquired on hospital admission, to predict the functional outcome of stroke treatment based on the modified Rankin Scale (mRS). This includes a fusion module to efficiently combine 3D non-contrast computed tomography (NCCT) features and clinical information. In comparative experiments using unimodal and multimodal data on the MRCLEAN dataset, we achieve a state-of-the-art AUC score of 0.85.
Original languageEnglish
Title of host publication20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
Publication statusAccepted/In press - 23 Jan 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena de Indias Convention Center, Cartagena de Indias, Colombia
Duration: 18 Apr 202321 Apr 2023
http://2023.biomedicalimaging.org/en/

Conference

Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Abbreviated titleISBI
Country/TerritoryColombia
CityCartagena de Indias
Period18/04/2321/04/23
Internet address

Keywords

  • Transformer
  • Multimodal
  • Stroke
  • Ischaemic
  • NCCT
  • Outcome

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