# Breast Histopathological Image Classification Based on Auto-Encoder Reconstructed Domain Adaptation
## 4 Experiments
### Result
#### 4.4.2 Comparative Experiment  
To validate the effectiveness of our proposed algorithm (AER-SSDA), we compared it with several other domain adaptation algorithms. The experiments utilized the BreakHis dataset and the SNL dataset to conduct bidirectional domain adaptation classification experiments, verifying the effectiveness of the method through these bidirectional experiments.  
The comparison algorithms included two unsupervised domain adaptation algorithms (DSN [33] and MCD_DA [34]), as well as two semi-supervised domain adaptation algorithms (FADA [35] and CCSA [36]). Among them, DSN and FADA are also reconstruction-based domain adaptation algorithms.  
The experimental results from the BreakHis dataset to the SNL dataset are shown in Table 4. The proposed AER-SSDA algorithm exhibited the best experimental results (F1 score: 93.40%, accuracy: 95.24%, sensitivity: 94.66%, specificity: 95.56%), fully demonstrating the effectiveness of this method. Due to the lack of target sample labels that can provide classification guidance during training, the unsupervised domain adaptation methods, DSN (F1 score: 71.58%, accuracy: 64.87%, sensitivity: 61.60%, specificity: 73.22%) and MCD_DA (F1 score: 73.34%, accuracy: 75.03%, sensitivity: 90.20%, specificity: 66.14%), performed significantly worse than the proposed AER-SSDA algorithm. Compared with the other two semi-supervised domain adaptation methods, CCSA (F1 score: 78.63%, accuracy: 78.07%, sensitivity: 80.12%, specificity: 76.35%) and FADA (F1 score: 85.86%, accuracy: 80.43%, sensitivity: 82.65%, specificity: 74.78%), the proposed AER-SSDA algorithm demonstrated a stronger learning ability in semi-supervised domain adaptation tasks. This is attributed to the proposed auto-encoder, which learns more useful information for classification within a single domain during the feature extraction and reconstruction task, enhancing the representation ability of intra-domain features. When aligning multiple feature representations during the encoding-decoding process, it measures and reduces the differences in feature distributions between the source and target domains. This facilitates the transfer of labeled feature representations from the source domain to the target domain, thereby improving the classification performance of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **F1 Score (\%)** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline DSN [33] & 71.58 \(\pm\) 0.78 & 64.87 \(\pm\) 1.24 & 61.60 \(\pm\) 2.31 & 73.22 \(\pm\) 1.72 \\ MCD\_DA [34] & 73.34 \(\pm\) 1.11 & 75.03 \(\pm\) 1.49 & 90.20 \(\pm\) 1.76 & 66.14 \(\pm\) 2.94 \\ FADA [35] & 85.86 \(\pm\) 0.88 & 80.43 \(\pm\) 0.60 & 82.65 \(\pm\) 0.92 & 74.78 \(\pm\) 0.89 \\ CCSA [36] & 78.63 \(\pm\) 0.69 & 78.07 \(\pm\) 0.73 & 80.12 \(\pm\) 1.08 & 76.35 \(\pm\) 1.43 \\ AER-SSDA & **93.40 \(\pm\) 0.48** & **95.24 \(\pm\) 0.32** & **94.66 \(\pm\) 0.87** & **95.56 \(\pm\) 1.27** \\ \hline \hline \end{tabular}  
* The bold numbers in the table represent the best comparison results.  
\end{table}
Table 4: The experimental results of different comparative algorithms (BreakHis to SNL)Table 5 presents the experimental results of the comparison algorithms from SNL to BreakHis. Due to the decrease in the number of source domain samples, leading to a reduction in the knowledge transferable to the target domain, the model's classification performance declined. However, compared with other comparison methods, the proposed AER-SSDA algorithm still demonstrated better performance. This further validates the effectiveness of the model and algorithm. The experiments showed that the proposed AER-SSDA achieved the best performance in breast histopathological image classification tasks.  
Figure 3: Feature visualization of different feature distribution of the target domain images (BreakHis to SNL: (**a–d**); SNL to BreakHis to SNL: (**e–h**)).