Papers › Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation
Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation
Xi Zhang, Zaiqiao Meng, Jake Lever, Edmond S. L. Ho
We introduce a radiology-focused visual language model designed to generate radiology reports from chest X-rays. Building on previous findings that large language models (LLMs) can acquire multimodal capabilities when aligned with pretrained vision encoders, we demonstrate similar potential with chest X-ray images. This integration enhances the ability of model to understand and describe chest X-ray images. Our model combines an image encoder with a fine-tuned LLM based on the Vicuna-7B architecture, enabling it to generate different sections of a radiology report with notable accuracy. The training process involves a two-stage approach: (i) initial alignment of chest X-ray features with the LLM (ii) followed by fine-tuning for radiology report generation.
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