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.Comment: Accepted at IEEE ISBI 2023, 5 page
