Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation

• URL: http://arxiv.org/abs/2412.04954v1
• Date: Fri, 06 Dec 2024 11:14:03 GMT
• Title: Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation
• Authors: Xi Zhang, Zaiqiao Meng, Jake Lever, Edmond S. L. Ho,
• Abstract summary: We introduce a radiology-focused visual language model designed to generate radiology reports from chest X-rays. 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.
• Score: 21.772106685777995
• License:
• Abstract: 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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