Year: 2026
Type: conference-paper
Abstract: Radiology report generation requires precise alignment between medical imaging findings and clinically coherent textual descriptions.While current methods predominantly rely on either large vision-language models (LVLMs) for visual grounding or large language models (LLMs) for medical narrative generation, they often fail to effectively integrate multimodal clinical evidence with domain-specific knowledge.This paper proposes a novel multimodal dual-path framework that synergistically combines LVLMs and LLMs to address these limitations.Our approach establishes a dynamic fusion between LVLMs' visual-semantic grounding capabilities and LLMs' clinical knowledge reasoning.Specifically, we employ a structured prompting strategy that models the report generation task into three clinically meaningful sections and introduces fine-grained multilabel classification prompts to guide the models, enabling more accurate and comprehensive clinical report generation.Experiments on the public MIMIC-CXR and IU-Xray benchmarks demonstrate our framework's superiority over state-of-the-art methods.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166845179