# Breast Histopathological Image Classification Based on Auto-Encoder Reconstructed Domain Adaptation
## 4 Experiments
### Result
#### 4.4.4 Discussion of Experimental Results  
The experimental results reveal that the proposed AER-SSDA method exhibited the best classification performance. Compared to unsupervised domain adaptation methods DSN and MCD_DA, it achieved significant improvements in the results, indicating that using a small number of labeled target samples can enhance domain adaptation performance. When compared to semi-supervised domain adaptation methods FADA and CCSA, the proposed AER-SSDA method demonstrated stronger capabilities in addressing overfitting and under-training issues. This may be attributed to the fact that the domain adaptation with auto-encoder reconstruction can more effectively learn classification-beneficial information from the extracted features. Compared to AER-Variant1, AER-Variant2, and AER-Variant3, feature reconstruction enriches the useful information for classification, while the similarity loss enhances the network's ability to extract information and minimizes cross-domain differences. The F1 score results of the comparative experiments and ablation experiments are presented in Figure 4 (BreakHis to SNL) and Figure 5 (SNL to BreakHis), which clearly demonstrate the performance advantages of the proposed AER-SSDA algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Loss**} & \multirow{2}{*}{**F1 Score (\%)**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**Sensitivity (\%)**} & \multirow{2}{*}{**Specificity (\%)**} \\ \cline{2-2} \cline{6-7}  & \(\mathcal{L}_{similarity}\) & & & & & & \\ \hline AER-Variant1 & & ✓ & ✓ & 87.02 \(\pm\) 0.86 & 84.17 \(\pm\) 0.95 & 82.40 \(\pm\) 1.43 & 88.13 \(\pm\) 1.37 \\ AER-Variant2 & ✓ & & ✓ & 83.89 \(\pm\) 1.21 & 81.23 \(\pm\) 1.41 & 75.53 \(\pm\) 2.38 & 87.80 \(\pm\) 1.89 \\ AER-Variant3 & ✓ & ✓ & & 81.17 \(\pm\) 1.76 & 77.26 \(\pm\) 1.83 & 68.71 \(\pm\) 3.41 & 88.75 \(\pm\) 1.53 \\ AER-SSDA & ✓ & ✓ & ✓ & **92.08 \(\pm\) 0.59** & **89.47 \(\pm\) 0.47** & **88.87 \(\pm\) 1.19** & **89.14 \(\pm\) 1.22** \\ \hline \hline \end{tabular}  
* In the table, a \(\blacktriangle\) below the loss item indicates that this loss exists, and the absence of a \(\blacktriangle\) represents that this loss item is discarded in the total loss function. The bold numbers in the table represent the best comparison results.  
\end{table}
Table 7: The experimental results of different ablation algorithms (SNL to BreakHis).  
Figure 4: Comparison of classification results with different methods (BreakHis to SNL).  
The statistical \(p\)-value tests based on accuracy for the AER-SSDA algorithm compared to the competitive and ablation algorithms are shown in Table 8 (BreakHis to SNL) and Table 9 (SNL to BreakHis). This indicates that the experimental results of the AER-SSDA algorithm are statistically significant compared to the comparative and ablation experiments, further proving that the AER-SSDA algorithm has more advantages than other algorithms in the feature reconstruction-based domain adaptation classification tasks.