Digital pathology images have long been regarded as the gold standard for cancer diagnosis in clinical medicine. A highly generalized digital pathological image diagnosis system can provide strong support for cancer diagnosis, help to improve the diagnostic efficiency and accuracy of doctors, and has important research value. The whole slide image of different centers can lead to very large staining differences due to different scanners and dyes, which pose a challenge to the generalization performance of the model application in multi-center data testing. In order to achieve the normalization of multi-center data, this paper proposes a style transfer algorithm based on an adversarial generative network for high-resolution images. The gradient-guided dye migration model proposed in this paper introduces a gradient-enhanced regularized term in the loss function design of the algorithm. A style transfer algorithm was applied to the source data, and the diagnostic performance of the multi-example learning model based on the domain data was significantly improved by validation in the pathological image datasets of two centers. The proposed method improved the AUC of the best classification model from 0.8856 to 0.9243, and another set of experiments improved the AUC from 0.8012 to 0.8313.

The consistency regularization method is a widely used semi-supervised method that uses regularization terms constructed from unlabeled data to improve model performance. Poor-quality target predictions in regularization terms produce noisy gradient flows during training, resulting in a degradation in model performance. Recent semi-supervised methods usually filter out low-confidence target predictions to alleviate this problem, but also prevent the model from learning features from unlabeled data in low-confidence regions. Specifically, in medical imaging and other cross-domain scenarios, models are prone to producing large numbers of low-confidence predictions. To improve the quality of target predictions while utilizing unlabeled data more efficiently, we propose an uncertainty-aware semi-supervised method that incorporates the breast anatomical prior, for pectoral muscle segmentation. Our method has a typical teacher-student dual model structure, where uncertainty is used to distinguish between high- and low-confidence predictions in the teacher model output. A low-confidence prediction refinement module was designed to refine the low-confidence predictions by incorporating high-confidence predictions and a learned anatomical prior. The anatomical prior, as regularization of the target predictions, was learned from annotations and an auxiliary task. The final target predictions are a combination of high-confidence teacher predictions and refined low-confidence predictions. The proposed method was evaluated on a dataset containing 635 data points from three data centers. Compared with the baseline method, the proposed method showed an average improvement in DICE index of 1.76, an average reduction in IoU index of 3.21, and an average reduction in HD index of 5.48. The experimental results show that our method generalizes well to the test set and outperforms other methods in all evaluation metrics.