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

Zeynel Abidin Samak1, Philip Clatworthy2,3 and Majid Mirmehdi1

1Dept. of Computer Science, University of Bristol, 2Translational Health Sciences, University of Bristol, 3Stroke Neurology, Southmead Hospital, North Bristol NHS Trust, Bristol, UK

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.

Poster

Downloads

Bibtex

@inproceedings{samak2023transop,
  title={TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction},
  author={Samak, Zeynel A. and Clatworthy, Philip and Mirmehdi, Majid},
  booktitle={2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)}, 
  year={2023},
  pages={1-5},
  doi={10.1109/ISBI53787.2023.10230576},
  organization={IEEE Computer Society}
}

Acknowledgements

The authors would like to thank the Principal Investigators of the MR CLEAN trial: Profs Aad van der Lugt, Diederik W.J. Dippel, Charles B.L.M. Majoie, Yvo B. W.E.M. Roos, Wim H. van Zwam and Robert J. van Oostenbrugge for providing the data. Zeynel Abidin Samak is funded by the Ministry of Education (1416/YLSY), the Republic of Turkiye.