Patient-specific haemodynamic assessment is essential for clarifying vascular pathophysiology and guiding clinical decision-making. While computational fluid dynamics offers high-fidelity flow quantification, its routine clinical use remains limited by the time-consuming iterative solution process required for patient-specific simulations. To tackle these issues, we developed a geometry-guided graph encoding network (G3e-Net), a CFD-supervised image-derived prediction framework that learns anatomically informed representations from CTA-derived vascular geometries. G3e-Net coupled centerline-guided spectral encoding to resolve global topology with multi-scale curvature descriptors capturing local boundary nuances. An attention mechanism then combined these different spatial representations, allowing the network to dynamically balance global connectivity with local geometry in complicated vascular structures. By learning a direct mapping from CTA-derived point clouds to three-dimensional haemodynamic fields, G3e-Net enables rapid functional estimation without requiring volumetric meshing or iterative numerical optimization during inference. Extensive validation on thoracic aortas and carotid arteries from multiple hospitals confirmed that G3e-Net outperformed state-of-the-art baselines, reducing prediction errors for velocity, pressure, and WSS by 10-20%. Notably, inference on high-resolution aortic geometries comprising approximately 200,000 nodes was completed in less than 4 seconds, providing substantial acceleration compared with conventional CFD. These results support the potential of anatomically informed graph representation learning for rapid image-derived haemodynamic analysis and patient-specific functional vascular assessment in thoracic aortic and carotid artery applications.
The automatic diagnosis of rare diseases via few-shot class-incremental learning (FSCIL) is critically hindered by severe data scarcity and the prevailing treatment of disease labels as a flat, unstructured space. This oversimplification neglects the rich taxonomic structures inherent in medical ontologies, leading to poor generalization on fine-grained subtypes. To bridge this gap, we propose Hierarchical Visual Prompt Disentanglement (HVPD), a hierarchy-guided framework for continual rare disease diagnosis. Unlike methods that use textual cues for flat semantic alignment, HVPD uses superclass and subclass category names to elicit visual-attribute semantics from a medical large language model (LLM) and converts the LLM’s pre-decoding representations into hierarchical semantic anchors that guide image-conditioned visual prompt generation. We design a hierarchy-aware disentanglement mechanism that synthesizes instance-adaptive visual prompts while reducing the overlap between superclass-common patterns and subclass-specific diagnostic cues through an orthogonality-based cross-level constraint. This design alleviates the stability–plasticity dilemma by injecting structured medical priors into the visual feature space. Extensive experiments on the SD-198 (dermatology) and RFMiD (ophthalmology) datasets demonstrate that HVPD consistently improves over existing FSCIL approaches, delivering stronger fine-grained discrimination and knowledge retention under extreme few-shot regimes.
