Summary of Transformer-based Classification Outcome Prediction For Multimodal Stroke Treatment, by Danqing Ma et al.
Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment
by Danqing Ma, Meng Wang, Ao Xiang, Zongqing Qi, Qin Yang
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multitrans framework combines non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment using the Transformer architecture and self-attention mechanism. The approach aims to predict functional outcomes of stroke treatment by leveraging various methods based on the Transformer architecture. Compared to single-modal text or image classification, multi-modal combination outperforms individual modalities, with the Transformer model performing worse on imaging data but improving when combined with clinical meta-diagnostic information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study combines NCCT images and discharge reports to predict stroke treatment outcomes using a Transformer-based framework. The results show that combining multiple modalities is better than using just one, and that the Transformer architecture can learn from both imaging and clinical data to make accurate predictions. |
Keywords
* Artificial intelligence * Image classification * Multi modal * Self attention * Transformer
