轻量模型在乳腺病变分割中兼顾精度与效率，适合实际部署。
Mammographic Lesion Segmentation with Lightweight Models: A Comparative Study
- 用MobileNetV2等轻量模型替代传统U-Net，降低计算开销。
- 最佳模型Dice达0.5766，参数量仅为U-Net的25%。
- 适用于资源受限环境的医疗辅助诊断系统。
乳腺癌是全球女性癌症致死的主要原因，乳腺钼靶摄影是主要筛查手段。尽管深度学习在病变分割上表现优异，但多数模型依赖高计算量架构，限制了其在资源受限环境中的应用。本研究评估了轻量级模型在乳腺病变分割中的性能与效率。对比了MobileNetV2、EfficientNet Lite、FPN和Fast-SCNN等架构与U-Net基线模型在INbreast数据集上的表现，采用5折交叉验证。评估指标包括Dice分数、交并比（IoU）和召回率，并分析模型复杂度。结果显示，加入挤压-激励模块的MobileNetV2表现最优，Dice分数达0.5766，参数量约为U-Net的25%。在跨数据集（DMID）测试中，因域偏移导致准确率下降，但召回率保持稳定。结果表明，轻量级架构可在性能与效率间取得实用平衡，适用于可部署的CAD系统。
原文摘要 · Abstract (English)
Breast cancer is a leading cause of cancer-related mortality among women worldwide, with mammography as the primary screening tool. While deep learning models have shown strong performance in lesion segmentation, most rely on computationally intensive architectures that limit their use in resource-constrained environments. This study evaluates the performance and efficiency of lightweight models for mammographic lesion segmentation. Architectures including MobileNetV2, EfficientNet Lite, FPN, and Fast-SCNN were compared against a U-Net baseline using the INbreast dataset with 5-fold cross-validation. Performance was assessed using Dice score, Intersection over Union (IoU), and Recall, alongside model complexity. MobileNetV2 with Squeeze-and-Excitation (SCSE) achieved the best performance, with a Dice score of 0.5766 while using approximately 75% fewer parameters than U-Net. Cross-dataset evaluation on the DMID dataset showed reduced accuracy due to domain shift but preserved recall. These results demonstrate that lightweight architectures offer a practical balance between performance and efficiency for deployable CAD systems.
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