  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.
