MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generation
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
Despite significant advancements in adapting Large Language Models (LLMs) for
radiology report generation (RRG), clinical adoption remains challenging due to
difficulties in accurately mapping pathological and anatomical features to
their corresponding text descriptions. Additionally, semantic agnostic feature
extraction further hampers the generation of accurate diagnostic reports. To
address these challenges, we introduce Medical Concept Aligned Radiology Report
Generation (MCA-RG), a knowledge-driven framework that explicitly aligns visual
features with distinct medical concepts to enhance the report generation
process. MCA-RG utilizes two curated concept banks: a pathology bank containing
lesion-related knowledge, and an anatomy bank with anatomical descriptions. The
visual features are aligned with these medical concepts and undergo tailored
enhancement. We further propose an anatomy-based contrastive learning procedure
to improve the generalization of anatomical features, coupled with a matching
loss for pathological features to prioritize clinically relevant regions.
Additionally, a feature gating mechanism is employed to filter out low-quality
concept features. Finally, the visual features are corresponding to individual
medical concepts, and are leveraged to guide the report generation process.
Experiments on two public benchmarks (MIMIC-CXR and CheXpert Plus) demonstrate
that MCA-RG achieves superior performance, highlighting its effectiveness in
radiology report generation.
