mlsanityData to 14 Oct 2026

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

arXiv 2301.10829 ↗First posted 25 Jan 2023eess.IV cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 26 of its 50 references that are in the map. First posted on arXiv 25 Jan 2023.

Builds on

26 of 50 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatmentcited by 11
  2. 2024Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectivescited by 2
  3. 2025Outcome prediction and individualized treatment effect estimation in patients with large vessel occlusion strokecited by 0