## 4 Experiment
### Performance Comparison
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline
**Split** & **Method** & **F1-Score** & **Precision (P)** & **Recall (R)** & **Recall (MIOU)** \\ \hline \multirow{4}{*}{1/4} & UNet & 0.6464 & 0.6818 & 0.6202 & 0.5096 \\  & FixMatch & 0.6717 & 0.6915 & 0.6586 & 0.5375 \\  & UniMatch & 0.6708 & 0.6754 & 0.6696 & 0.5346 \\  & CSRA-Net & 0.6854 & 0.7002 & 0.6758 & 0.5465 \\ \hline \multirow{4}{*}{1/8} & UNet & 0.6180 & 0.6475 & 0.5973 & 0.4803 \\  & FixMatch & 0.6551 & 0.6871 & 0.6304 & 0.5198 \\  & UniMatch & 0.6518 & 0.6910 & 0.6224 & 0.5181 \\  & CSRA-Net & 0.6639 & 0.7028 & 0.6376 & 0.5283 \\ \hline \multirow{4}{*}{1/16} & UNet & 0.5446 & 0.6376 & 0.5176 & 0.4253 \\  & FixMatch & 0.5829 & 0.6596 & 0.5541 & 0.4645 \\  & UniMatch & 0.6039 & 0.6544 & 0.5850 & 0.4779 \\  & CSRA-Net & 0.6158 & 0.6653 & 0.5971 & 0.4912 \\ \hline \multirow{4}{*}{1/32} & UNet & 0.4908 & 0.5121 & 0.4749 & 0.3758 \\  & FixMatch & 0.6006 & 0.6379 & 0.5909 & 0.4668 \\ \cline{1-1}  & UniMatch & 0.6082 & 0.6468 & 0.5859 & 0.4733 \\ \cline{1-1}  & CSRA-Net & 0.6183 & 0.6574 & 0.5956 & 0.4828 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison experiment results on the PanNuke dataset. Figure 4: MIOU scores of different methods on the PanNuke dataset. Table 3 illustrates the experimental results of CSRA-Net compared to fully supervised methods and the other state-of-the-art semi-supervised approaches on the Conic dataset. Comparable to the findings on the PanNuke dataset, both FixMatch and UniMatch demonstrated weaker performance relative to CSRA-Net. The segmentation accuracy results of these two networks under various conditions were similar, with an average improvement margin between them being less than 1.5%. Specifically, CSRA-Net exhibited an approximate 5% higher average accuracy across different conditions compared to FixMatch and approximately a 2% improvement compared to UniMatch. Compared to UNet, CSRA-Net demonstrated substantial improvements across all data partitions, particularly in the 1/8 and 1/16 scenarios, where the F1-score increased by 8.26% and 9.21%, and the MIOU improved by 5.82% and 6.05%, respectively. When compared to FixMatch, CSRA-Net exhibited consistent enhancements across all data proportions, with notable performance gains in the 1/8 and 1/32 scenarios, where the F1-score rose by 2% and %, and the MIOU increased by 0.7% and 2.7%, respectively. This highlights the superior effectiveness of the semi-supervised strategy SWC proposed in this work over FixMatch. In comparison to UniMatch, CSRA-Net achieved a moderate yet stable improvement across all data partitions, with the F1-score increasing by 1.6-1.9% and the MIOU improving by 0.7-1.3%. These results indicate that CSRA-Net offers distinct advantages in semi-supervised learning scenarios. To provide a more intuitive illustration of the MIOU scores for various methods under different proportions of labeled data in the Conic dataset, Figure 5 offers a visual representation. The results shown in the figure demonstrate that CSRA-Net achieved superior segmentation performance across all four scenarios. In summary, the experiments conducted on the PanNuke and Conic datasets show that CSRA-Net is capable of effectively leveraging limited labeled data to improve segmentation performance. Notably, in scenarios with extremely low labeling ratios (1/8, 1/16, and 1/32), it consistently outperformed other semi-supervised methods (FixMatch and UniMatch) by maintaining stable performance gains. These results validate the effectiveness and strong generalization capability of CSRA-Net in the task of semi-supervised cell instance segmentation.