This paper investigates the impact of breast density distribution on the generalization performance of deep-learning models on mammography images using the VinDr-Mammo dataset. We explore the use of domain adaptation techniques, specifically Domain Adaptive Object Detection (DAOD) with the Noise Latent Transferability Exploration (NLTE) framework, to improve model performance across breast densities under noisy labeling circumstances. We propose a robust augmentation framework to bridge the domain gap between the source and target inside a dataset. Our results show that DAOD-based methods, along with the proposed augmentation framework, can improve the generalization performance of deep-learning models (+5% overall mAP improvement approximately in our experimental results compared to commonly used detection models). This paper highlights the importance of domain adaptation techniques in medical imaging, particularly in the context of breast density distribution, which is critical in mammography.

翻译：本文利用VinDr-Mammo数据集，研究了乳腺密度分布对深度学习模型在乳腺摄影图像上泛化性能的影响。我们探索了领域自适应技术的应用，特别是结合噪声潜移迁移性探索（NLTE）框架的领域自适应目标检测（DAOD），以在噪声标注环境下提升模型在不同乳腺密度间的性能。我们提出了一种鲁棒的数据增强框架，以弥合数据集内源域与目标域之间的领域差距。实验结果表明，基于DAOD的方法结合所提出的增强框架，能够提升深度学习模型的泛化性能（与常用检测模型相比，在我们的实验结果中整体mAP约提升5%）。本文强调了领域自适应技术在医学影像中的重要性，尤其是在乳腺摄影中关键的乳腺密度分布语境下。