TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction
This work addresses the critical need for accurate patient selection in stroke treatment to improve outcomes, though it is incremental as it builds on existing multimodal and transformer methods.
The paper tackled the problem of predicting functional outcomes for acute ischaemic stroke treatment by proposing TranSOP, a transformer-based multimodal network that combines clinical metadata and 3D imaging data, achieving a state-of-the-art AUC score of 0.85 on the MRCLEAN dataset.
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.