## 4 Experiment
### Ablation Experiments
#### 4.4.2 The Function of the Combination Module  
In the preceding ablation experiments, the designed methods were individually integrated into the baseline network to validate their standalone contributions. The results demonstrate the effectiveness of the proposed methods in addressing the complex task of cell instance segmentation. In the subsequent ablation experiments, a detailed analysis was conducted on the performance improvements achieved by different module combinations under varying proportions of labeled data. As shown in Table 5, the Base+SWC+RAA scheme consistently outperformed the Base+CAS+SWC scheme across all data partitions, achieving higher F1-score and MIOU improvements. This indicates that the RAA component plays a more critical role in enhancing cell feature extraction and fine-grained information learning. Specifically, under the 1/4, 1/8, 1/16, and 1/32 data partitions, the F1-score of Base+SWC+RAA improved by 1.30%, 0.90%, 1.99%, and 1.92%, respectively, compared to the baseline network. Similarly, the MIOU index improved by 1.18%, 1.20%, 4.25%, and 2.55%, respectively.  
Furthermore, CSRA-Net (Base + CAS + SWC + RAA) demonstrated the best performance across all experiments. Compared to the baseline model, this network achieved an average accuracy improvement of 1.5%, outperforming the enhancement observed among mainstream networks and thereby validating the effectiveness of the combined module. Specifically, in the 1/16 data split, its F1-score and MIOU improved by 5.64% and 5.75%, respectively, compared to the baseline. This highlights the significant enhancement in the network's learning capability achieved through the synergistic interaction of  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Split** & **Method** & **F1-Score** & **Precision (P)** & **Recall (R)** & **Recall (MIOU)** \\ \hline \multirow{4}{*}{1/4} & Base & 0.6717 & 0.6915 & 0.6586 & 0.5375 \\  & Base+CAS+SWA & 0.6768 & 0.6957 & 0.6642 & 0.5412 \\  & Base+SWA+RAA & 0.6805 & 0.6998 & 0.6678 & 0.5438 \\  & BBase+CAS+SWA+RAA (CSRA-Net) & 0.6854 & 0.7002 & 0.6758 & 0.5465 \\ \hline \multirow{4}{*}{1/8} & Base & 0.6551 & 0.6871 & 0.6304 & 0.5198 \\  & Base+CAS+SWA & 0.6593 & 0.6908 & 0.6350 & 0.5254 \\  & Base+SWA+RAA & 0.6610 & 0.6925 & 0.6371 & 0.5261 \\  & BBase+CAS+SWA+RAA (CSRA-Net) & 0.6639 & 0.7028 & 0.6376 & 0.5283 \\ \hline \multirow{4}{*}{1/16} & Base & 0.5829 & 0.6596 & 0.5541 & 0.4645 \\  & Base+CAS+SWA & 0.5902 & 0.6679 & 0.5619 & 0.4797 \\ \cline{1-1}  & Base+SWA+RAA & 0.5945 & 0.6720 & 0.5661 & 0.4842 \\ \cline{1-1}  & BBase+CAS+SWA+RAA (CSRA-Net) & 0.6158 & 0.6653 & 0.5971 & 0.4912 \\ \hline \multirow{4}{*}{1/32} & Base & 0.6006 & 0.6379 & 0.5909 & 0.4668 \\ \cline{1-1}  & Base+CAS+SWA & 0.6084 & 0.6460 & 0.5982 & 0.4754 \\ \cline{1-1}  & Base+SWA+RAA & 0.6121 & 0.6502 & 0.6018 & 0.4787 \\ \cline{1-1}  & BBase+CAS+SWA+RAA (CSRA-Net) & 0.6183 & 0.6574 & 0.5956 & 0.4828 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the ablation experiments of the combined modules on the PanNuke dataset.  
multiple modules in scenarios with limited labeled data. However, in the 1/32 data split, despite achieving the highest improvement among all methods (the F1-score increased by 2.95%, and the MIOU increased by 3.43%), the performance gain was relatively smaller compared to the 1/16 split. This indicates that under conditions of extremely scarce labeled data, the model's overall performance is constrained, leading to a saturation effect in the contributions of additional modules.