Breast Cancer Diagnosis Method Based on Phase Congruency and Dual-Branch Feature Modeling

Breast cancer histopathological image classification remains a challenging task because reliable diagnosis depends on both fine-grained local lesion characteristics and multi-scale global tissue structures. However, current deep learning approaches often face challenges in effectively integrating these complementary cues, particularly in the presence of staining variations, ambiguous lesion boundaries, and limited annotated datasets. To address these challenges, we propose a novel method called UNI-Phase-Dual Network (UPDNet). This approach enhances the detection of stable lesion boundaries and subtle patterns by incorporating phase congruency, while combining it with global tissue information using the UNI foundation model. The method utilizes two branches to process features from different perspectives, one focusing on fine details and the other capturing broader context. Additionally, we apply a fine-tuning strategy that improves generalization and reduces overfitting in scenarios with small datasets. Experiments on three widely used breast cancer datasets, BRACS, BreakHis, and BACH, demonstrate that UPDNet significantly outperforms existing methods. Specifically, on the 7-class BRACS task, UPDNet achieves 68.58% accuracy, which is a 2.21% improvement over previous methods, and an increase of 1.48% in the weighted F1 score. These results demonstrate the strong potential of UPDNet in breast cancer histopathological image classification.