Category-weight instance fusion learning for unsupervised domain adaptation on breast cancer histopathology images

Breast cancer is one of the most common malignant tumors among women, and early diagnosis can significantly mitigate its impact. Despite substantial advancements in breast cancer diagnosis using deep learning methods, many challenges persist. In clinical practice, transferring trained deep learning models to new, unlabeled patient samples is essential but challenging due to substantial variability among patient domains. Furthermore, existing domain adaptation models often neglect class-aware sub-domain gaps. Additionally, variations in image styles across domains further impede the accurate diagnostic in the target domain. To address these issues, we propose the category-weight instance fusion learning model for unsupervised domain adaptation in breast cancer diagnosis. This model employs a category-weighted contrast knowledge distillation module to align domains at the category level by selectively clustering similar samples and segregating dissimilar ones. Simultaneously, the meticulously designed instance-aware feature mixing module merges image styles across domains through a domain feature mixing algorithm, significantly enhancing breast cancer domain adaptation capability. Results on BreakHis and ICIAR-2018 datasets demonstrate that our model outperforms other stateof-the-art domain adaptation algorithms in diagnostic accuracy, proving the transferability and robustness of our model across diverse clinical patients.