Intracranial aneurysm rupture causes subarachnoid hemorrhage with mortality near 50%, making early detection critical. Although CTA enables rapid screening, detecting small aneurysms within the complex three-dimensional branching of the Circle of Willis remains expertise-dependent. Existing automated systems are constrained by class imbalance, skull-base artifacts that mimic vascular contrast, and reliance on global binary classification without structured localization, limiting surgical relevance and interpretability. We propose CMTF-Net, a cross-modal target fusion framework that reframes aneurysm screening as anatomically structured reasoning. By supervising 14 vascular territories independently, the network encodes Circle of Willis geometry while allowing multi-segment activation, aligning model design with clinical workflow. CMTF-Net achieves near-perfect AUC-ROC with narrow confidence intervals and sustained precision under imbalance. Grad-CAM and causal maps show spatially localized activation along major arteries, supporting interpretable, anatomically grounded screening in low-data settings.

翻译：颅内动脉瘤破裂导致蛛网膜下腔出血，死亡率接近50%，因此早期检测至关重要。尽管CTA能够实现快速筛查，但在威利斯环复杂的三维分支结构中检测小型动脉瘤仍需依赖专家经验。现有自动化系统受限于类别不平衡、模拟血管对比度的颅底伪影，以及依赖缺乏结构化定位的全局二分类，限制了手术相关性和可解释性。我们提出CMTF-Net，一种跨模态目标融合框架，将动脉瘤筛查重新定义为解剖结构化的推理过程。通过独立监督14个血管区域，该网络编码了威利斯环的几何结构，同时允许多节段激活，使模型设计与临床工作流程保持一致。CMTF-Net在窄置信区间内实现了近乎完美的AUC-ROC，并在不平衡条件下保持稳定精度。Grad-CAM和因果图显示沿主要动脉的空间定位激活，支持低数据场景下可解释、解剖学基础的筛查。