Summary of Huatuogpt-vision, Towards Injecting Medical Visual Knowledge Into Multimodal Llms at Scale, by Junying Chen et al.
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
by Junying Chen, Chi Gui, Ruyi Ouyang, Anningzhe Gao, Shunian Chen, Guiming Hardy Chen, Xidong Wang, Ruifei Zhang, Zhenyang Cai, Ke Ji, Guangjun Yu, Xiang Wan, Benyou Wang
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 This research paper presents advancements in medical multimodal large language models (MLLMs) by refining medical image-text pairs from PubMed and employing GPT-4V to denoise and reformat the data. The resulting dataset, PubMedVision, contains 1.3 million medical visual question answering (VQA) samples. Validation shows that PubMedVision can significantly enhance the medical multimodal capabilities of current MLLMs, achieving better performance in benchmarks like the MMMU Health & Medicine track. The authors also train a 34B medical MLLM called HuatuoGPT-Vision, which outperforms open-source MLLMs in medical multimodal scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better machines that can understand and work with medical images and text together. It starts by cleaning up and fixing problems with existing data from PubMed. Then it uses a special kind of language model called GPT-4V to make the data even cleaner and more useful. The result is a huge dataset called PubMedVision, which has 1.3 million examples of medical visual question answering (VQA). This new data helps make current machines better at working with medical images and text. The researchers also create a special machine that can use this data to get really good at doing medical tasks. |
Keywords
* Artificial intelligence * Gpt * Language model * Question answering
