Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models
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 ...
| Authors | Yingshu Li, Yunyi Liu, Zhanyu Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 August 2026 |
| Last updated at source | 16 September 2026 |
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