Robust multi-domain digital pathology image segmentation via joint balancing representation learning

Multi-domain learning (MDL) seeks to mitigate performance degradation caused by domain-specific feature shifts between training and testing environments. In breast cancer digital pathology, such shifts result from variations in staining protocols, imaging devices, and tissue preparation. Existing MDL methods focus on aligning feature discrepancies across domains, often neglecting inter-domain feature balance caused by data distribution disparities. Addressing this balance is essential for effective cross-domain learning in breast cancer pathology, particularly in clinically diverse settings. Additionally, leveraging multi-source training data to enhance model adaptability across pathological domains remains challenging. We introduce a Joint Training Strategy (JTS) and a novel breast cancer digital pathology dataset with expert annotations to capture pathological heterogeneity across multiple domains. We propose Differential Ratio Integration with Twin-domain Training (DRIFT) for multi-source digital pathology image segmentation, addressing domain adaptation through: (1) PRISM-DD, a dynamic data distribution mechanism that balances domain contributions to optimize segmentation, and (2) AMBiCoL, a multi-level loss function integrating adaptive boundary masks with bidirectional consistency modeling to enhance generalization. Experiments on two breast cancer digital pathology datasets demonstrate that DRIFT outperforms state-of-the-art methods, supporting its potential for robust, scalable multi-domain segmentation in computational pathology. Code is available at https://github.com/Joycecc123/DRIFT .