## 5 Discussion and Conclusions  
This paper presents CSRA-Net, a segmentation network based on category-adaptive and attention mechanisms designed for semi-supervised instance segmentation of cell nuclei. To address the prediction bias arising from class imbalance in semi-supervised instance segmentation, we proposed a category-adaptive sampling method. By dynamically adjusting the sampling strategy for labeled data, this method enhances the balance of samples across different cell types, allowing the model to effectively learn the characteristics of various cell types and consequently produce more accurate segmentation results. Secondly, to tackle the segmentation robustness challenge encountered by semi-supervised models, we proposed a strong-weak contrast consistency strategy. This strategy compels the model to disregard noise and irrelevant background information in images while emphasizing the learning of structural and morphological features of cells. Consequently, it enables the extraction of more robust and intrinsic feature representations, thereby improving the model's robustness. Finally, we proposed a region-adaptive attention mechanism that adaptively adjusts the attention weights based on regional complexity. This enables the model to focus more effectively on complex regions, thereby enhancing the accuracy of segmentation. The proposed network, acting as a universal semi-supervised instance segmentation method, not only provides valuable insights for both semi-supervised and fully supervised segmentation approaches but also effectively tackles the challenges of sample category imbalance and limited segmentation robustness in segmentation tasks.  
Despite the satisfactory performance of CSRA-Net, several limitations should be noted. First, the method heavily relies on the quality of pseudo-labels generated during the semi-supervised learning process. Although our strong-weak contrast consistency strategy helps mitigate the impact of noise in pseudo-labels, the potential accumulation of errors in the pseudo-labels remains a challenge. These errors could lead to suboptimal model performance, particularly in complex or ambiguous regions of the images. Additionally, while the dynamic category-adaptive sampling method effectively addresses the issue of class imbalance, it requires careful hyperparameter tuning to ensure optimal performance across different datasets. For instance, the inverse proportional strategy used in our method to calculate the oversampling factor for each class may not achieve ideal balance in cases of extreme class imbalance between cell types.  
In future work, we will explore strategies to further improve pseudo-label quality, such as incorporating self-training techniques or refining the adaptive sampling method to better handle extreme class imbalances. Furthermore, reducing the computational overhead of the region-adaptive attention mechanism could enhance the applicability of CSRA-Net in more resource-constrained environments.  
Methodology, X.L. and D.L.; Software, Z.W. and J.L.; Validation, Z.Y.; Formal analysis, Z.Y. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China under Grant 62376089.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in this article. All codes and trained models are available at [[https://github.com/ldrunning/Semi-supervised-segmentation](https://github.com/ldrunning/Semi-supervised-segmentation)]([https://github.com/ldrunning/Semi-supervised-segmentation](https://github.com/ldrunning/Semi-supervised-segmentation)) (accessed on 27 April 2025). Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.