## 4 Experiment
### Performance Comparison  
We compared our method with the state-of-the-art methods on the PanNuke and Conic datasets. Both datasets encompass a variety of cell types and exhibit a significant imbalance across these types. As such, they are considered among the most challenging instance segmentation datasets in terms of segmentation complexity. In this experiment, we evaluated the performance of CSRA-Net and compared it with that of the fully supervised network UNet as well as the state-of-the-art semi-supervised methods Fixmatch and Unimatch. All methods were tested under identical conditions, including the same dataset and training settings, to ensure a fair comparison. Furthermore, detailed comparisons were conducted for scenarios where the proportion of labeled data was set to 1/4, 1/8, 1/16, and 1/32, respectively. Table 2 summarizes the experimental results of the network on the PanNuke dataset. As shown in the table, CSRA-Net achieved superior performance across all annotation ratios (1/4, 1/8, 1/16, and 1/32). The performance outcomes of both FixMatch and UniMatch networks were outperformed by CSRA-Net. Despite being among the state-of-the-art semi-supervised segmentation networks, there existed a noticeable time gap between the introduction of these two models. Nonetheless, their segmentation accuracies were comparable, with the improvement margin between them being less than 0.5%. This suggests that multi-type instance segmentation under complex backgrounds poses an exceptionally high level of difficulty, where even highly sophisticated network designs can only yield marginal accuracy gains. Compared to UNet, CSRA-Net demonstrated significant improvements at all annotation data ratios, particularly in scenarios with extremely limited annotations (1/32), where the F1-score and MIOU increased by 12.75% and 10.70%, respectively. This highlights its effectiveness in learning from small sample annotation data. When compared to FixMatch, CSRA-Net exhibited a relatively modest improvement (approximately 1%) at higher annotation ratios (1/4 and 1/8). However, at lower annotation ratios (1/16 and 1/32), the F1-score increased by 3.29% and 1.77%, while the MIOU improved by 2.67% and 1.60%, respectively. This indicates that CSRA-Net outperforms FixMatch in scenarios withscarce data. In comparison to UniMatch, CSRA-Net maintained a consistent lead across all data ratios, albeit with slightly smaller gains than those observed against FixMatch. Specifically, at the 1/16 and 1/32 data divisions, the improvements in F1-score and MIOU ranged between 1.0% and 1.3%, suggesting that the two methods performed similarly, yet CSRA-Net retained a stable advantage. In contrast to the marginal improvement observed between FixMatch and UniMatch, CSRA-Net achieved an enhancement of approximately 2% across all scenarios, thereby providing further evidence of its effectiveness. To provide a more intuitive illustration of the MIOU scores for various methods under different proportions of labeled data in the PanNuke dataset, Figure 4 offers a visual representation. The results shown in the figure demonstrate that CSRA-Net achieved superior segmentation performance across all four scenarios.