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arXiv · 2301.10829

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

Zeynel A. Samak·Philip Clatworthy·Majid Mirmehdi
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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.

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Zeynel A. Samak, Philip Clatworthy, Majid Mirmehdi. 2023-01-25. TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction. https://doi.org/10.1109/isbi53787.2023.10230576

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