SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913509753749504 |
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| author | Wu, Jinge Kim, Yunsoo Shi, Daqian Cliffton, David Liu, Fenglin Wu, Honghan |
| author_facet | Wu, Jinge Kim, Yunsoo Shi, Daqian Cliffton, David Liu, Fenglin Wu, Honghan |
| contents | Inspired by the success of large language models (LLMs), there is growing research interest in developing LLMs in the medical domain to assist clinicians. However, for hospitals, using closed-source commercial LLMs involves privacy issues, and developing open-source public LLMs requires large-scale computational resources, which are usually limited, especially in resource-efficient regions and low-income countries. We propose an open-source Small Language and Vision Assistant (SLaVA-CXR) that can be used for Chest X-Ray report automation. To efficiently train a small assistant, we first propose the Re$^3$Training method, which simulates the cognitive development of radiologists and optimizes the model in the Recognition, Reasoning, and Reporting training manner. Then, we introduce a data synthesis method, RADEX, which can generate a high-quality and diverse training corpus with privacy regulation compliance. The extensive experiments show that our SLaVA-CXR built on a 2.7B backbone not only outperforms but also achieves 6 times faster inference efficiency than previous state-of-the-art larger models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_13321 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation Wu, Jinge Kim, Yunsoo Shi, Daqian Cliffton, David Liu, Fenglin Wu, Honghan Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Inspired by the success of large language models (LLMs), there is growing research interest in developing LLMs in the medical domain to assist clinicians. However, for hospitals, using closed-source commercial LLMs involves privacy issues, and developing open-source public LLMs requires large-scale computational resources, which are usually limited, especially in resource-efficient regions and low-income countries. We propose an open-source Small Language and Vision Assistant (SLaVA-CXR) that can be used for Chest X-Ray report automation. To efficiently train a small assistant, we first propose the Re$^3$Training method, which simulates the cognitive development of radiologists and optimizes the model in the Recognition, Reasoning, and Reporting training manner. Then, we introduce a data synthesis method, RADEX, which can generate a high-quality and diverse training corpus with privacy regulation compliance. The extensive experiments show that our SLaVA-CXR built on a 2.7B backbone not only outperforms but also achieves 6 times faster inference efficiency than previous state-of-the-art larger models. |
| title | SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation |
| topic | Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.13321 |