Summary of Gla-ai4biomed at Rrg24: Visual Instruction-tuned Adaptation For Radiology Report Generation, by Xi Zhang et al.
Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation
by Xi Zhang, Zaiqiao Meng, Jake Lever, Edmond S. L. Ho
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 We present a novel visual language model tailored to generating radiology reports from chest X-rays. Building on previous research demonstrating multimodal capabilities in large language models, we integrate vision encoders with our model, enhancing its ability to comprehend and describe chest X-ray images. Our approach combines an image encoder with a fine-tuned Vicuna-7B-based large language model, allowing it to generate accurate radiology reports with distinct sections. The training process involves a two-stage approach: initial alignment of chest X-ray features with the LLM, followed by fine-tuning for report generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer that can read and write medical reports about chest X-rays! This new technology combines images from X-rays with special language models to create accurate medical reports. It’s like having a super smart assistant that can help doctors quickly summarize what they see in the X-ray pictures. The model uses two steps: first, it learns how to understand X-ray images and then it fine-tunes its writing skills to produce detailed reports. This breakthrough could make it easier for doctors to quickly review patient information. |
Keywords
» Artificial intelligence » Alignment » Encoder » Fine tuning » Language model » Large language model
