Summary of Biomedical Visual Instruction Tuning with Clinician Preference Alignment, by Hejie Cui et al.
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
by Hejie Cui, Lingjun Mao, Xin Liang, Jieyu Zhang, Hui Ren, Quanzheng Li, Xiang Li, Carl Yang
First submitted to arxiv on: 19 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 paper proposes a novel framework, Biomedical Visual Instruction Tuning with Clinician Preference Alignment (BioMed-VITAL), to adapt multimodal foundation models trained for general usage to the biomedical domain. The approach incorporates clinician preferences into both stages of generating and selecting instruction data for tuning biomedical multimodal foundation models. Specifically, it uses GPT-4V generator prompted with diverse demonstrations selected by clinicians, followed by a separate selection model that distills clinician and policy-guided model preferences into a rating function to select high-quality data. The resulting model demonstrates significant improvements in open visual chat (18.5% relatively) and medical VQA (win rate up to 81.73%). This work has important implications for the development of AI models capable of understanding and reasoning with biomedical information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help doctors and researchers better understand medical images and text. Right now, AI models can do a good job at recognizing patterns in images or text, but they often don’t understand what those patterns mean in the context of medicine. The authors propose a new way to train these AI models by giving them examples of how humans would describe medical information, and then having the model learn from those examples. This approach leads to significant improvements in the model’s ability to answer questions about medical images and text, which could be very helpful for doctors and researchers trying to diagnose diseases or develop new treatments. |
Keywords
» Artificial intelligence » Alignment » Gpt » Instruction tuning
