用拓扑特征增强乳腺钼靶模型，提升跨设备检测准确率。
Topological Conditioning for Mammography Models via a Stable Wavelet-Persistence Vectorization
- 通过小波-持久性向量化提取图像拓扑结构，生成稳定特征图。
- 在葡萄牙数据集上，模型患者级AUC从0.55提升至0.75。
- 适合关注医学影像泛化能力的医生与算法工程师。
乳腺癌是全球女性中最常见的癌症，也是主要致死原因。筛查乳腺钼靶可降低死亡率，但诊断仍存在大量假阴性和假阳性，且模型在不同设备、模态和人群间部署时性能常下降。本文提出一种基于小波持久性向量化的拓扑条件信号，用于提升模型外部泛化能力。通过拓扑数据分析，提取跨灰度阈值持续存在的图像结构，并转化为对微小强度扰动具有理论稳定性的小波空间多尺度特征图。这些特征图通过通道拼接方式融入两阶段检测流程。模型在美国CBIS DDSM胶片数字化数据集上训练并验证，在葡萄牙INbreast和中国CMMD两个独立全数字乳腺钼靶数据集上评估，结果以患者为单位报告。在INbreast数据集上，使用小波持久性通道增强ConvNeXt Tiny模型，患者级AUC由0.55提升至0.75，且在有限训练预算下实现显著改进。
原文摘要 · Abstract (English)
Breast cancer is the most commonly diagnosed cancer in women and a leading cause of cancer death worldwide. Screening mammography reduces mortality, yet interpretation still suffers from substantial false negatives and false positives, and model accuracy often degrades when deployed across scanners, modalities, and patient populations. We propose a simple conditioning signal aimed at improving external performance based on a wavelet based vectorization of persistent homology. Using topological data analysis, we summarize image structure that persists across intensity thresholds and convert this information into spatial, multi scale maps that are provably stable to small intensity perturbations. These maps are integrated into a two stage detection pipeline through input level channel concatenation. The model is trained and validated on the CBIS DDSM digitized film mammography cohort from the United States and evaluated on two independent full field digital mammography cohorts from Portugal (INbreast) and China (CMMD), with performance reported at the patient level. On INbreast, augmenting ConvNeXt Tiny with wavelet persistence channels increases patient level AUC from 0.55 to 0.75 under a limited training budget.
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