## 4 Experiment
### Ablation Experiments
#### 4.4.1 The Function of Individual Modules  
Category-adaptive sampling (CAS) enhances the balance of samples across different cell types by dynamically adjusting the sampling strategy for labeled data. This indirectly optimizes the training process of unlabeled data and mitigates the prediction errors caused by class imbalance. The balanced labeled data provide a more uniform supervision signal, enabling the model to comprehensively learn the features of various cell types and generate higher-quality pseudo-labels. This reduces error propagation due to insufficient minority-class samples. During this process, the features learned from balanced batches avoid over-reliance on discriminative patterns of dominant cell types, thereby reducing the likelihood of misclassifying ambiguous cells in unlabeled data as majority classes and improving segmentation and classification accuracy. The benchmark network selected for this study was Deeplabv3. As a prominent model in the segmentation domain, Deeplabv3 demonstrated exceptionally high segmentation accuracy. Even when applied to the highly challenging PanNuke dataset, Deeplabv3 achieved segmentation accuracy comparable to that of state-of-the-art semi-supervised segmentation networks. In recent years, the average improvement achieved by mainstream semi-supervised segmentation networks on the PanNuke dataset has been approximately 0.5%, with no substantial performance gains observed. As shown in Table 4, when the proportion of labeled data was 1/4, 1/8, 1/16, and 1/32, the F1-score increased by 1.07%, 0.9%, 1.22%, and 1.25%, respectively, and the MIOU score increased by 0.33%, 0.67%, 0.16%, and 0.49%, respectively, after applying the category adaptive sampling strategy in the benchmark network. Compared to the accuracy gains achieved by existing semi-supervised instance segmentation networks, the improvement  
Figure 5: MIOU scores of different methods on the Conic dataset. brought by the category-adaptive sampling strategy is notably more significant, thereby demonstrating its effectiveness. To verify the strong-weak contrast consistency (SWC) strategy, two strongly perturbed features were generated for an unlabeled cell image sample. Their feature consistency was constrained via contrastive learning to ensure robustness in data augmentation and feature extraction. This strategy compels the model to disregard noise and irrelevant background information in the image, focusing instead on learning the structural and morphological features of the cells, thereby extracting more robust and essential feature representations. In this study, strong perturbations were applied to labeled data to encourage the model to learn perturbation-invariant feature representations, enhancing its adaptability to variations in cell morphology. By applying strong perturbations to both labeled and unlabeled data, the proposed method promotes consistency in the model's feature space, alleviating optimization bias caused by differences in perturbations between supervised and unsupervised data and achieving a robust integration of supervised and unsupervised signals. As shown in Table 4, after introducing the perturbation strategy, the model's performance improved by 1.31%, 1.24%, 1.77%, and 1.65% in the F1-score and by 0.54%, 0.83%, 0.23%, and 1.05% in the MIOU score compared to the baseline network when the proportion of labeled data was 1/4, 1/8, 1/16, and 1/32, respectively. These results demonstrate the effectiveness of this strategy in enhancing the model's robustness. Compared to the category-adaptive sampling strategy, the strong-weak contrast consistency strategy demonstrated a more pronounced improvement in accuracy, thereby validating its effectiveness in enhancing model robustness. The region-adaptive attention (RAA) mechanism dynamically adjusts attention according to the complexity of regions, enabling the model to prioritize complex areas. This enhances the accuracy and robustness of cell segmentation while mitigating the adverse effects of erroneous learning during model training.