Summary of Dia-llama: Towards Large Language Model-driven Ct Report Generation, by Zhixuan Chen et al.
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
by Zhixuan Chen, Luyang Luo, Yequan Bie, Hao Chen
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medical report generation in computed tomography (CT) has been largely underexplored due to the high-dimensional nature of CT images and limited availability of CT-report pairs. However, recent advancements in large language models (LLMs) have shed light on addressing existing challenges in medical report generation. Specifically, the proposed Dia-LLaMA framework adapts LLaMA2-7B for CT report generation by incorporating diagnostic information as guidance prompts. This approach leverages a pre-trained ViT3D with perceiver to extract visual information from CT scans and extracts additional diagnostic information by referring to a disease prototype memory bank. Furthermore, the model introduces disease-aware attention to enable adjusting attention for different diseases. Experimental results on the chest CT dataset demonstrate that Dia-LLaMA outperforms previous methods and achieves state-of-the-art performance on both clinical efficacy metrics and natural language generation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical report generation is important in medicine because it helps doctors write accurate reports about patients’ conditions. Current report generation systems are not very good at this task, especially when dealing with CT scans. A new approach called Dia-LLaMA might help improve this situation. It uses a special kind of computer program to analyze CT scans and write reports that are more accurate and detailed. The program is trained on many examples of CT scans and report pairs, which helps it learn what makes a good report. The results show that Dia-LLaMA does a better job than other approaches at generating reports that are both clinically effective and well-written. |
Keywords
» Artificial intelligence » Attention » Llama
