RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection
Journal:
arXiv
Published Date:
May 20, 2025
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in
various domains, including radiology report generation. Previous approaches
have attempted to utilize multimodal LLMs for this task, enhancing their
performance through the integration of domain-specific knowledge retrieval.
However, these approaches often overlook the knowledge already embedded within
the LLMs, leading to redundant information integration and inefficient
utilization of learned representations. To address this limitation, we propose
RADAR, a framework for enhancing radiology report generation with supplementary
knowledge injection. RADAR improves report generation by systematically
leveraging both the internal knowledge of an LLM and externally retrieved
information. Specifically, it first extracts the model's acquired knowledge
that aligns with expert image-based classification outputs. It then retrieves
relevant supplementary knowledge to further enrich this information. Finally,
by aggregating both sources, RADAR generates more accurate and informative
radiology reports. Extensive experiments on MIMIC-CXR, CheXpert-Plus, and IU
X-ray demonstrate that our model outperforms state-of-the-art LLMs in both
language quality and clinical accuracy
