Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models

2026-08-25

AI Summary

为解决放射报告生成中临床准确度问题，提出图监督层次临床对齐方法，通过疾病中心对齐和全局临床语义对齐提高报告准确性。

Abstract

Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically faithful generation remains challenging because current supervision is still imposed mostly at the report level. This creates a granularity mismatch: radiology reports are composed of disease-grounded findings, while existing methods are trained mainly with whole-report objectives. To address this problem, we propose Graph-Supervised Hierarchical Clinical Alignment, which reformulates image-report supervision as a hierarchical clinical alignment problem. Our method structures this alignment as a disease-conditioned process, where supervision is decomposed into two levels: Disease-Centric Alignment for fine-grained disease-specific correspondence, and Global Clinical Semantic Alignment for report-level semantic coherence. A clinical knowledge graph is used as a training-time-only structural prior that defines disease-specific supervision units and their clinical relationships, introducing no additional overhead at inference. Because standard contrastive alignment could produce false negatives when studies share overlapping pathologies, we combine instance-conditioned discriminative matching with disease-conditioned soft regularization, enabling fine-grained yet clinically consistent cross-modal representations. Experiments on MIMIC-CXR, IU-Xray, and COV-CTR show that our method consistently improves performance on both conventional and clinical metrics. Notably, our 3B model surpasses several prior systems with larger 7B/13B backbones, suggesting that improving supervision structure, rather than increasing model size, can be more effective for RRG.

Problem

• Radiology Report Generation
• Clinical Faithfulness
• Granularity Mismatch
• Hierarchical Clinical Alignment
• Disease-Grounded Findings

Innovation

• Graph-Supervised Hierarchical Clinical Alignment
• Disease-Centric Alignment
• Global Clinical Semantic Alignment
• Clinical Knowledge Graph
• Cross-Modal Representations