层次注意力用于亚临床角膜塑形症稀疏体积异常检测
Hierarchical Attention for Sparse Volumetric Anomaly Detection in Subclinical Keratoconus
AI总结：
本文提出层次注意力机制，用于亚临床角膜塑形症的稀疏体积异常检测，显著提升灵敏度和特异性，优于传统CNN和ViT模型。
摘要：
在体积医学影像中检测弱的、空间分布的异常仍然具有挑战性，因为将细微信号整合到非相邻区域存在困难。本研究对十六种架构进行了受控比较，涵盖卷积、混合和变换器家族，用于从三维前段光学相干断层扫描（AS-OCT）中检测亚临床角膜塑形症。结果表明，层次架构在灵敏度和特异性上分别提高了21-23%，特别是在困难的亚临床阶段，优于卷积神经网络（CNNs）和全局注意力视觉变换器（ViT）基线。机理分析表明，这种优势来自于空间尺度对齐：层次窗口产生有效的感受野，与亚临床异常的中间范围匹配，避免了卷积模型中的过度局部性和纯全局注意力的弥散整合特性。注意力距离测量显示，亚临床病例需要比健康或明显病理体积更长的空间整合，层次模型表现出更低的方差和更解剖学一致的聚焦。表征相似性进一步表明，层次注意力学习了一个独特的特征空间，平衡了局部结构敏感性和灵活的长距离交互。辅助年龄和性别预测任务显示出中等程度的跨任务一致性，支持这些归纳原则的可推广性。研究结果为体积异常检测提供了设计指导，并突显了层次注意力作为医学影像早期病理变化分析的原理性方法。
英文摘要：
The detection of weak, spatially distributed anomalies in volumetric medical imaging remains challenging due to the difficulty of integrating subtle signals across non-adjacent regions. This study presents a controlled comparison of sixteen architectures spanning convolutional, hybrid, and transformer families for subclinical keratoconus detection from three-dimensional anterior segment optical coherence tomography (AS-OCT). The results demonstrate that hierarchical architectures achieve 21-23% higher sensitivity and specificity, particularly in the difficult subclinical regime, outperforming both convolutional neural networks (CNNs) and global-attention Vision Transformer (ViT) baselines. Mechanistic analyses indicate that this advantage arises from spatial scale alignment: hierarchical windowing produces effective receptive fields matched to the intermediate extent of subclinical abnormalities, avoiding the excessive locality observed in convolutional models and the diffuse integration characteristic of pure global attention. Attention-distance measurements show that subclinical cases require longer spatial integration than healthy or overtly pathological volumes, with hierarchical models exhibiting lower variance and more anatomically coherent focus. Representational similarity further indicates that hierarchical attention learns a distinct feature space that balances local structure sensitivity with flexible long-range interactions. Auxiliary age and sex prediction tasks demonstrate moderately high cross-task consistency, supporting the generalizability of these inductive principles. The findings provide design guidance for volumetric anomaly detection and highlight hierarchical attention as a principled approach for early pathological change analysis in medical imaging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
