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

• URL: http://arxiv.org/abs/2301.10829v1
• Date: Wed, 25 Jan 2023 21:05:10 GMT
• Title: TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction
• Authors: Zeynel A. Samak, Philip Clatworthy, Majid Mirmehdi
• Abstract summary: We propose a transformer-based multimodal network (TranSOP) for a classification approach that employs clinical metadata and imaging information. 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, we achieve a state-of-the-art AUC score of 0.85.
• Score: 2.358784542343728
• License: http://creativecommons.org/licenses/by/4.0/
• 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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