# Breast Histopathological Image Classification Based on Auto-Encoder Reconstructed Domain Adaptation
## 4 Experiments
### Result
#### 4.4.3 Ablation Experiment  
To demonstrate the effectiveness of each key component in the algorithm, we designed three ablation variants: AER-Variant1, AER-Variant2, and AER-Variant3, for comparison with the proposed algorithm. Compared to the proposed algorithm, AER-Variant1 removes the domain similarity loss generated during the domain discrimination process; in other words, it removes loss term \(\mathcal{L}_{similarity}\). AER-Variant2 explores the impact of the auto-encoder structure on experimental results by conducting domain adaptation training without reconstruction loss; in other words, it removes loss term \(\mathcal{L}_{recon}\). AER-Variant3 removes both the domain discriminator, the auto-encoder and the corresponding loss function; in other words, it removes loss term \(\mathcal{L}_{MMD}\).  
Table 6 presents the experimental results of the ablation variants from BreakHis to SNL. On the BreakHis -to-SNL dataset, the experimental results of the three ablation variants, AER-Variant1 (F1 score: 88.17%, accuracy: 90.96%, sensitivity: 93.33%, specificity: 89.63%), AER-Variant2 (F1 score: 84.70%, accuracy: 88.57%, sensitivity: 87.98%, specificity: 88.89%), and AER-Variant3 (F1 score: 83.32%, accuracy: 87.62%, sensitivity: 85.33%, specificity: 88.86%), demonstrated the necessity and importance of each part of the model. The results of AER-Variant1 showed that the similarity loss generated by the domain discriminator could blur the domain discriminative information in the encoder's feature representations and enhance the model's overall adversarial learning capability. AER-Variant2 replaced the auto-encoder embedding with a fully connected network with the same network parameters. The experimental results indicate that the proposed auto-encoder reconstruction domain adaptation model is not merely a simple compressed representation for feature extraction. In a series of feature transformations during encoding and decoding, the auto-encoder learns abstract information about histopathological images under the constraint of feature reconstruction loss, which can more effectively mine and learn the classification information of the samples. The experimental results of AER-Variant3 demonstrated that the domain similarity loss and high-level feature reconstruction loss could cooperate with each other. The analysis of the above ablation variants proves the effectiveness of the overall algorithm and its components in the domain adaptation experiment from BreakHis to SNL.  
Table 7 presents the experimental results of the ablation variants from SNL to BreakHis. Due to the reduction in the number of source domain samples, the model's classification performance declined. However, compared to other ablation variants, the proposed AER-SSDA  
\begin{table}
\begin{tabular}{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 & & ✓ & ✓ & 88.17 \(\pm\) 0.71 & 90.96 \(\pm\) 0.67 & 93.33 \(\pm\) 1.21 & 89.63 \(\pm\) 1.37 \\ AER-Variant2 & ✓ & & ✓ & 84.70 \(\pm\) 1.25 & 88.57 \(\pm\) 1.01 & 87.98 \(\pm\) 2.12 & 88.89 \(\pm\) 1.46 \\ AER-Variant3 & ✓ & ✓ & & 83.32 \(\pm\) 1.63 & 87.62 \(\pm\) 2.09 & 85.33 \(\pm\) 2.75 & 88.86 \(\pm\) 1.94 \\ 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}  
* In the table, a \(\blackcheck{\blackcheck{\blackcheck{\blackcheck{\blackcheck{\blackcheck{\blackcheck{ \blackcheck{\blackcheck{\blackcheck{\blackblackalgorithm still exhibited a better performance. This further demonstrates the effectiveness of each part of the model and algorithm. Every component in the model and loss terms is important and indispensable, and the absence of any one of them can lead to a decrease in model performance.