Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment
<|reference_start|>Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment: 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..<|reference_end|>
@article{ma2024transformer-based,
    title={Transformer-Based Classification Outcome Prediction for Multimodal
  Stroke Treatment},
    author={Danqing Ma, Meng Wang, Ao Xiang, Zongqing Qi, Qin Yang},
    journal={arXiv preprint arXiv:2404.12634},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12634},
    primaryClass={cs.CV cs.AI cs.LG}
}