Generalize Beyond the Lens: Deep Magnification Adaptation in Breast Cancer Histopathology

Cross-magnification variability poses a critical challenge in deploying AI models for digital pathology, as classifiers trained at a single resolution often fail to generalize across heterogeneous imaging conditions. This study systematically investigates the effects of magnification domain shifts on breast histopathology classification by combining handcrafted machine-learning features, modern deep architectures, and progressive domain adaptation strategies. Although handcrafted models performed well in the domain, they degraded sharply under cross-magnification transfer, and shallow alignment methods such as CORAL and MMD yielded only marginal improvements. In contrast, deep transformer and convolutional backbones demonstrated greater robustness through hierarchical, scale-tolerant feature learning. Adversarial domain adaptation with DANN yielded the most consistent cross-magnification generalization (AUC = 0.975) and preserved malignant recall, a critical clinical requirement. Gradient…