CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation
Existing CT report generation methods predominantly rely on global image features, neglecting fine-grained anatomical relationships among organs—thereby limiting diagnostic accuracy. To address this, we propose an anatomy-guided hierarchical graph modeling framework: (1) constructing a three-level graph structure—organ, organ system, and patient—based on anatomical masks; (2) designing a hierarchical graph attention network that explicitly fuses organ-level and global features extracted by a 3D pretrained encoder; and (3) integrating a large language model for structured report generation. This work is the first to enable interpretable, fine-grained anatomical knowledge modeling in CT report generation. Evaluated on the CT-RATE dataset, our method achieves an absolute F1-score improvement of 7.9% over state-of-the-art approaches. The implementation is publicly available.