Maintaining the performance of a deep learning model trained for breast cancer detection on a specific scanner type is challenging in a multi-scanner setting due to domain shifts caused by variations in imaging data. This often results in a performance drop when models trained on one scanner are tested on another. While re-training with labeled data from the new scanner is an option, delays in obtaining ground-truth labels make this approach impractical. To overcome this limitation, Unsupervised Domain Adaptation (UDA) offers a promising alternative by enabling models to adapt across scanners without requiring labeled target data. In this study, we investigated Conditional Domain-Adversarial Network (CDAN), an adversarial UDA approach, to adapt a classifier trained on Siemens scanner data using nearly 3 million mammograms from the Norwegian breast cancer screening program. We compared it to Maximum Mean Discrepancy (MMD), a simpler statistical feature alignment method, and evaluated histogram matching, which required no additional training. Our findings showed that the AUC drop on the target GE data (0.96 to 0.62) without adaptation was mitigated by histogram matching (AUC 0.84), but that was less effective than MMD (AUC 0.87), which performed competitively with CDAN. Further ablation with Domain-Adversarial Neural Network (DANN), the foundation of CDAN, suggested limitations in the domain discriminator. Unlike prior work focusing solely on performance, we paired UDA with explainability. This revealed how feature relevance shifted across scanner domains, offering novel insights into model generalizability in cancer detection.