这篇论文用拓扑方法解决了小动脉瘤检测的假阳性难题，SECT在<3mm病灶上比传统方法准得多，还能跨CT品牌用，做医学AI的值得看看。
针对CTA中颅内动脉瘤自动检测假阳性高的问题，论文提出拓扑感知的假阳性减少框架，使用平滑欧拉特征变换（SECT）编码3D血管几何。在RSNA 2025数据集上，SECT对直径<3mm的小动脉瘤检测AUC达0.943，灵敏度78.5%（95%特异度），远超方向无关方法（AUC约0.68）。该表示对CT扫描仪型号不敏感，在四家厂商的留一验证中平均AUC 0.927。
Topological Shape Representation for Aneurysm -- Bifurcation Detection
Automated detection of intracranial aneurysms (IAs) from CT angiography (CTA) is severely hindered by high false-positive rates. Convolutional neural networks (CNNs) rely on local pixel intensities, causing systematic confusion between saccular aneurysms and vascular bifurcations -- a problem especially acute for small lesions (<3 mm), where detection sensitivity falls below 60%. We propose a plug-and-play, topology-aware false-positive reduction framework evaluating the Smooth Euler Characteristic Transform (SECT) -- a directional representation encoding global 3D vascular geometry independently of intensity -- against persistence-based summaries (Persistence Images and Landscapes), tested on a stratified subset of the RSNA 2025 dataset. SECT achieves an AUC of 0.943, substantially outperforming direction-agnostic methods (AUC ~0.68), and exhibits a clinical performance inversion: it excels on the sub-3 mm cohort, maintaining 0.943 AUC and 78.5% sensitivity at 95% specificity. The representation is also scanner-agnostic, achieving 0.927 mean AUC under leave-one-scanner-out (LOGO) validation across four manufacturers. By capturing asymmetric geometric invariants rather than intensity profiles, SECT reliably resolves the primary structural confounder in IA detection, positioning it as a robust downstream filter for hybrid deep-learning diagnostic pipelines.