PASAformer: Cerebrovascular Disease Classification with Medical Prior-Guided Adapter and Pathology-Aware Sparse Attention

Cerebrovascular diseases (CVDs) such as aneurysms, arteriovenous malformations, stenosis, and Moyamoya disease are major public health concerns. Accurate classification of these conditions is essential for timely intervention, yet current computer-aided methods often exhibit limited representational capacity, feature redundancy, and insufficient interpretability, restricting clinical applicability. We propose PASAformer, a Swin-Transformer-based framework for cerebrovascular disease classification on Digital Subtraction Angiography (DSA). PASAformer incorporates a Pathology-Aware Sparse Attention (PASA) module that emphasizes lesion-related regions while suppressing background redundancy. Inserted into the Swin backbone, PASA replaces dense window self-attention, improving computational efficiency while preserving the hierarchical architecture. We further employ the MiAMix data augmenter to increase sample diversity, and incorporate a CombinedAdapter encoder that injects anatomical priors from the frozen Medical Segment Anything Model (MED-SAM) into early-stage representations, strengthening discriminative power under limited supervision. To support research in this underexplored area, we curate CDSA-NEO, a proprietary DSA dataset comprising more than 1,700 static images across four major cerebrovascular disease categories, constituting the first large-scale benchmark of its kind. Furthermore, an external cohort of angiographic runs with sequential, unselected frames is used to assess robustness in realistic temporal workflows. Extensive experiments on CDSA-NEO and public vascular datasets demonstrate that PASAformer achieves competitive precision and balanced accuracy compared to representative state-of-the-art models, while providing more focused visual explanations. These results suggest that PASAformer can support automated cerebrovascular disease classification on angiography, and that CDSA-NEO provides a benchmark for future method development and evaluation.