Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment

Danqing Ma, Meng Wang, Ao Xiang, Zongqing Qi, Qin Yang
4/19/2024
cs.CVcs.AIcs.LG

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Abstract

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach to predicting functional outcomes of stroke treatment. The results show that the performance of single-modal text classification is significantly better than single-modal image classification, but the effect of multi-modal combination is better than any single modality. Although the Transformer model only performs worse on imaging data, when combined with clinical meta-diagnostic information, both can learn better complementary information and make good contributions to accurately predicting stroke treatment effects..

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Cite this paper

@article{ma2024transformerbased,
  title  = {Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment},
  author = {Danqing Ma and Meng Wang and Ao Xiang and Zongqing Qi and Qin Yang},
  year   = {2024},
  eprint = {2404.12634},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.12634v3}
}

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