Summary of Slava-cxr: Small Language and Vision Assistant For Chest X-ray Report Automation, by Jinge Wu et al.
SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation
by Jinge Wu, Yunsoo Kim, Daqian Shi, David Cliffton, Fenglin Liu, Honghan Wu
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 an open-source Small Language and Vision Assistant (SLaVA-CXR) for automating Chest X-Ray report generation, addressing the limitations of using large language models (LLMs) in medical settings. The proposed Re^3Training method simulates radiologists’ cognitive development to optimize model training in recognition, reasoning, and reporting tasks. Additionally, the RADEX data synthesis method generates a diverse and high-quality training corpus while ensuring compliance with privacy regulations. Experimental results show that SLaVA-CXR outperforms previous state-of-the-art models by 6 times in terms of inference efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a special computer program to help doctors write reports for X-ray pictures more efficiently. The problem is that big language models are not suitable for medical use because they’re not open-source and require a lot of computing power, which can be hard to find in some areas. To solve this issue, the authors propose a new method called Re^3Training that trains the model in three stages: recognition, reasoning, and reporting. They also developed a way to create a large training dataset while keeping patient information private. The results show that their program can write reports just as well as bigger models but is much faster. |
Keywords
» Artificial intelligence » Inference
