DoSReMC：基于批量归一化适应的领域偏移鲁棒乳腺分类
DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation
AI总结：
本文提出DoSReMC框架，通过仅微调BN和FC层提升跨领域泛化能力，结合对抗训练进一步提升模型性能，适用于多种临床环境。
摘要：
大量基于深度学习的解决方案已用于自动识别乳腺癌，但其性能在跨领域应用时下降，主要由于领域偏移。本文提出DoSReMC框架，通过批量归一化适应提升跨领域泛化能力，利用三个大规模FFDM数据集进行系统评估，结果显示BN层是领域依赖的主要来源，通过仅微调BN和FC层并结合对抗训练，有效提升模型性能并降低计算成本。
英文摘要：
Numerous deep learning-based solutions have been developed for the automatic recognition of breast cancer using mammography images. However, their performance often declines when applied to data from different domains, primarily due to domain shift - the variation in data distributions between source and target domains. This performance drop limits the safe and equitable deployment of AI in real-world clinical settings. In this study, we present DoSReMC (Domain Shift Resilient Mammography Classification), a batch normalization (BN) adaptation framework designed to enhance cross-domain generalization without retraining the entire model. Using three large-scale full-field digital mammography (FFDM) datasets - including HCTP, a newly introduced, pathologically confirmed in-house dataset - we conduct a systematic cross-domain evaluation with convolutional neural networks (CNNs). Our results demonstrate that BN layers are a primary source of domain dependence: they perform effectively when training and testing occur within the same domain, and they significantly impair model generalization under domain shift. DoSReMC addresses this limitation by fine-tuning only the BN and fully connected (FC) layers, while preserving pretrained convolutional filters. We further integrate this targeted adaptation with an adversarial training scheme, yielding additional improvements in cross-domain generalizability while reducing the computational cost of model training. DoSReMC can be readily incorporated into existing AI pipelines and applied across diverse clinical environments, providing a practical pathway toward more robust and generalizable mammography classification systems.
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