Fig. 7: Overview of AMAP framework.

The proposed method consists of three key components: (i) Anatomy-guided MAE pretraining, which biases masked reconstruction toward vascular regions to learn aneurysm-sensitive representations; (ii) Prompt-guided fine-tuning, where both shared and domain-adaptive prompts are injected into transformer layers to enhance small-lesion sensitivity and cross-domain robustness; and (iii) Boundary-aware domain generalization, where contrastive alignment on vessel boundaries with GS-EMA ensures consistent performance across domains. The outputs are precise intracranial aneurysm (IA) detection and segmentation, with improved boundary preservation and reduced false positives.