Summary of R2gencsr: Retrieving Context Samples For Large Language Model Based X-ray Medical Report Generation, by Xiao Wang et al.
R2GenCSR: Retrieving Context Samples for Large Language Model based X-ray Medical Report Generation
by Xiao Wang, Yuehang Li, Fuling Wang, Shiao Wang, Chuanfu Li, Bo Jiang
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper proposes a novel framework for X-ray medical report generation, leveraging the success of Large Language Models (LLMs). The existing methods adopt Transformers to extract visual features and feed them into LLMs for text generation. However, there is an urgent need to improve performance by extracting more effective information. Additionally, visual Transformer models bring high computational complexity. To address these issues, this paper introduces Mamba as the vision backbone with linear complexity, comparable to strong Transformer models. The framework also incorporates context retrieval from training sets and LLMs for generating medical reports. Experiments on three datasets (IU-Xray, MIMIC-CXR, CheXpert Plus) validate the effectiveness of the proposed model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make X-ray medical report generation better by using Large Language Models (LLMs). Right now, most methods use Transformers to get information from X-ray images and then feed that into LLMs for text reports. But there’s a problem: these methods don’t do a great job of extracting the right information for the LLMs. They also take up a lot of computer power. To fix this, the authors suggest using Mamba as the main image processor, which is faster and works just as well. The new method also looks at context from previous reports to help the LLMs generate better text reports. |
Keywords
* Artificial intelligence * Text generation * Transformer
