## 4 Experiments
### Experimental Results  
To thoroughly assess the effectiveness of our UCDA approach, we conducted validation experiments on the PCam and LC25000 datasets using transfer learning from the NCT-CRC-HE-100K pre-trained model. To establish a comprehensive comparison, we also incorporated three state-of-the-art unsupervised methods, namely Rotation [34], DeepCluster [35], and DARC [36], which employ fine-tuning on the labeled data.  
As presented in Table 1, the experimental results report the performance of our UCDA approach in various labeled data ratios. Given 1% labeled data, the UCDA method demonstrates higher accuracy and F1 scores compared to Rotation, DeepCluster, and DARC. The UCDA method achieves an accuracy of 86.83% (PCam) and 90.91% (LC25000), with corresponding F1 scores of 86.60% (PCam) and 90.89% (LC25000); At a 10% labeled data ratio, the UCDA method continues to exhibit superior performance. It achieves an accuracy of 88.65% (PCam) and 96.59% (LC25000), with F1 scores of 88.68% (PCam) and 96.52% (LC25000). Compared to other methods, while the UCDA method's accuracy on LC25000 is slightly lower than DARC and DeepCluster, it still outperforms Rotation; At a 50% labeled data ratio, the UCDA method achieves high accuracy and F1 scores on both PCam and LC25000. Its accuracy reaches 89.78% (PCam) and 96.36% (LC25000), with corresponding F1 scores of 89.63% (PCam) and 96.30% (LC25000). It is worth noting that although the UCDA method's accuracy on LC25000 is slightly lower than DARC, it remains superior to them on PCam. As for the NCT-CRC-HE-100K dataset, our UCDA obtains significant advantages compared to DeeCluster [35] and DARC [36], where it surpasses DARC with 2.03% accuracy and 2.09% F1-score on 1% labeled data. These results verify that the proposed UCDA has obvious superiority compared to existing methods in the source-free domain adaptation models.  
To summarize, the UCDA method demonstrates high performance at different labeled data ratios, providing better classification accuracy than other methods. These results highlight the potential and effectiveness of the UCDA method in the field of medical image analysis, offering strong support for further research and applications.  
The ROC Performance of UCDA  
To assess the effectiveness of the UCDA method in medical image classification, we conducted ROC curve analysis on the PCam and LC25000 datasets with 1% labeled data. The corresponding ROC curves are presented in Figure 3a,b. ROC curves are widely used in signal detection theory for evaluating classification systems, providing valuable insights into their performance.  
ROC analysis enables us to make objective and unbiased decisions, irrespective of costs or benefits. It allows us to visualize the trade-off between sensitivity (true positive rate) and specificity (1--false positive rate) at different decision thresholds, thereby assessing the discriminatory power of the UCDA method under various scenarios.  
As depicted in Figure 3a,b, the ROC curves for the PCam and LC25000 datasets exhibit AUC values of 0.936 and 0.963, respectively. These results indicate that the UCDA method demonstrates excellent performance in medical image classification tasks on these datasets. Moreover, the AUC values exceeding 0.9 further reinforce the superiority, robustness, and generalization capability of the proposed UCDA method.