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.
@article{arxiv.2301.10829,
title = {TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction},
author = {Zeynel A. Samak and Philip Clatworthy and Majid Mirmehdi},
journal= {arXiv preprint arXiv:2301.10829},
year = {2024}
}