Hierarchical Heterogeneous Aggregation Network for Multi-Shape Coronary Stenosis Detection in X-Ray Angiography Sequences

Accurate detection of multi-shape coronary artery stenoses from X-ray angiography (XRA) sequences plays a crucial role in diagnosing and planning interventions for coronary artery disease. However, vessel overlap, background noise, and nonlinear cardiac motion introduce significant challenges. These factors often result in missed detections, intra-frame class conflict, and temporal category drift, particularly for subtle and morphologically complex stenoses such as focal and bifurcation stenoses. To address these challenges, we propose a Hierarchical Heterogeneous Aggregation Network that effectively integrates both spatial and temporal cues across XRA sequences. The proposed framework incorporates a Channel Importance-guided Fusion module, which aims to enhance the representation of small-stenosis features by dynamically selecting high-importance channels across scales. Furthermore, we introduce a Hierarchical Heterogeneous Aggregator designed to reduce spatial redundancy and explicitly generate discriminative features across frames based on heterogeneous relationships, thereby improving temporal consistency and classification robustness. Existing experiments conducted on two clinical datasets indicate that our method outperforms existing detectors and stenosis methods in terms of detection accuracy and generalization.