EAAI Journal 2026 Journal Article
DawnNet: Domain-augmented multi-weighting network for endometrial histopathological image classification
- Fengjun Zhao
- Lin Wu
- Yi Li
- Xuelei He
- Hongyan Du
- Yanrong Chen
- Xiaowei He
- Yuqing Hou
Histopathological examination is the gold standard for diagnosing endometrial tissues, including normal endometrium, endometrial polyps, endometrial hyperplasia, and endometrial adenocarcinoma. However, subtle variations in gland-to-stroma ratios and nuclear morphology make the diagnosis subjective and dependent on pathologist expertise. Computer-aided diagnosis systems using deep learning-based approaches can improve diagnostic efficiency by automatically extracting representative features. However, their performance often degrades when encountering data variations from different institutes—a domain shift issue that violates the independent and identically distributed assumption between training and testing data. This out-of-distribution challenge is not fully addressed by existing domain generalization methods, which often overlook key morphological features essential for histopathological interpretation. To address this issue, we propose DawnNet, a domain-augmented multi-weighting network for robust endometrial histopathological image classification. DawnNet incorporates a domain augmentation module to improve generalization, a spatial–channel weighting attention module to enhance discriminative features while suppressing domain-specific ones, a sample weighting module to reduce spurious correlations, and a hybrid objective function to learn domain-invariant and diagnosis-relevant features. Experiments on publicly available datasets demonstrate that DawnNet outperforms state-of-the-art methods, showing promising generalization for both in-distribution and out-of-distribution cases. Codes are available at https: //github. com/aliy-ali/DawnNet.