Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment

2024-04-19 · Danqing Ma, Meng Wang, Ao Xiang, Zongqing Qi, Qin Yang

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..

📄 PDF Abstract BibTeX arXiv:2404.12634

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Tasks

Diagnosticimage-classification Image Classification Prediction text-classification Text Classification

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