Deep learning models enhance breast cancer detection in mammograms but struggle with domain shifts, where test data differ from training data. Domain adaptation (DA) helps address this issue but often relies on unstable adversarial techniques. Breast lesion classification in mammographic images also faces challenges like data scarcity and overfitting. Mixup mitigates these by generating synthetic samples, increasing variability, and improving robustness. Meanwhile, contrastive learning enhances feature alignment, boosting generalization and classification accuracy across domains. This paper proposes a DA model that integrates mixup and contrastive learning to improve feature alignment and generalization, leading to more accurate breast lesion classification. Our approach outperforms standard DA methods, achieving 82.5% accuracy, 0.774 F1 score, and 0.7868 AUC on INbreast (target dataset), surpassing DANN (63.6%) and Deep CORAL (67.7%). It also generalizes well, reaching 70.4% accuracy on CMMD and 63.64% on CDD-CESM, demonstrating its effectiveness in addressing domain shifts.