标题： DoSReMC: 使用批量归一化适应的域偏移鲁棒乳腺X线摄影分类 显示英文标题

标题： DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation

摘要： 基于深度学习的解决方案已被开发用于使用乳腺X线摄影图像自动识别乳腺癌。 然而，当应用于不同领域的数据时，它们的性能通常会下降，主要是由于领域偏移——源领域和目标领域之间数据分布的变化。 这种性能下降限制了人工智能在现实临床环境中的安全和公平部署。 在本研究中，我们提出 DoSReMC（领域偏移鲁棒性乳腺X线分类），一种批量归一化（BN）适应框架，旨在在不重新训练整个模型的情况下增强跨领域泛化能力。 使用三个大规模全视野数字乳腺X线摄影（FFDM）数据集——包括HCTP，一个新引入的、病理确认的内部数据集——我们对卷积神经网络（CNN）进行了系统的跨领域评估。 我们的结果表明，BN层是领域依赖性的主要来源：当训练和测试在同一领域内进行时，它们表现良好，并且在领域偏移下显著损害模型的泛化能力。 DoSReMC通过仅微调BN和全连接（FC）层，同时保留预训练的卷积滤波器来解决这一局限性。 我们进一步将这种有针对性的适应与对抗训练方案相结合，从而在跨领域泛化能力上取得了额外的改进。 DoSReMC可以轻松集成到现有的AI流程中，并应用于各种临床环境，为更强大和通用的乳腺X线分类系统提供了一条实用的路径。 显示英文摘要

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. 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.