GRIPNet：高斯径向强度先验引导的CT肺结节检测架构
GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT
- College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对CT中小于6毫米肺结节检测难的问题，提出基于高斯径向强度先验的GRIPNet检测器，通过风车卷积、双频模块等提升性能，在三个基准上mAP@0.5达95.3%以上。
AI中文摘要：
肺癌导致的死亡人数超过任何其他恶性肿瘤，低剂量CT筛查是早期诊断的主要途径。这一途径依赖于最小的病灶，然而小于六毫米的结节仍然难以检测，因为大多数方法将结节视为通用对象，忽略了其外观背后的成像物理原理。我们表明这种外观具有高度规律性。强度在结节的几何中心达到峰值，并呈高斯模式径向衰减，对来自三个公共基准的18,218个标注病灶进行拟合，在每个数据集和大小分层中，平均径向决定系数均高于0.86。方形卷积在两个轴上均匀采样，与这种径向信号不匹配，对于小结节尤其严重。基于这一证据，我们提出了GRIPNet（高斯径向强度先验网络），这是一种检测器，其中每个模块都映射到强度分布的可测量属性。风车卷积分解径向梯度，双频模块分离边界细节与结构上下文，膨胀掩码注意力匹配衰减范围，自适应损失根据显著性对样本重新加权。GRIPNet在KanserSet、LUNA16和Lung-PET-CT-Dx上将mAP@0.5分别提升至95.3、91.6和97.9个百分点，同时以实时速度增强了高IoU定位能力。
英文摘要：
Lung cancer causes more deaths than any other malignancy, and low-dose CT screening is the main pathway to early diagnosis. That pathway hinges on the smallest lesions, yet nodules below six millimeters remain hard to detect, because most methods treat a nodule as a generic object and ignore the imaging physics behind its appearance. We show that this appearance is highly regular. Intensity peaks at the geometric center of a nodule and decays radially in a Gaussian pattern, and a fit to 18,218 annotated lesions from three public benchmarks yields a mean radial coefficient of determination above 0.86 in every dataset and size stratum. A square convolution samples both axes uniformly and is mismatched to this radial signal, most severely for small nodules. Guided by this evidence, we propose GRIPNet (Gaussian Radial Intensity Prior Network), a detector in which every module maps to a measurable property of the intensity distribution. Pinwheel convolutions decompose radial gradients, a dual-frequency module separates boundary detail from structural context, dilated masked attention matches the decay extent, and an adaptive loss reweights samples by conspicuity. GRIPNet raises mAP@0.5 to 95.3, 91.6 and 97.9 percent on KanserSet, LUNA16 and Lung-PET-CT-Dx while sharpening high-IoU localization at real-time speed.
