# Semi-Supervised Medical Image Classification Combined with Unsupervised Deep Clustering
## 4 Experiments
### Results
#### 4.4.2 Comparison on ICIAR BACH 2018 Dataset  
We trained a fully supervised model with all labeled data as the upper benchmark and a fully supervised model with 10% labeled data as the lower benchmark. Table 3 shows the comparison of various methods under different percentages of labeled data, and it can be observed that the model with more advanced data augmentation strategies (Auto Augment, CT Augment, Rand Augment, etc.) can perform better. At the same time, it can be noted that with the decrease in labeled data, the performance attenuation of our method is the least obvious, which reflects that our method has better generalization performance  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{2-5}  & **AUC** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Baseline & 85.17 & 89.42 & 63.11 & 87.25 \\ \hline TCSE & 87.15 & 89.91 & 65.56 & 90.05 \\ \hline TE & 87.23 & 90.05 & 65.69 & 90.14 \\ \hline MT & 89.56 & 91.36 & 67.35 & 91.22 \\ \hline UDA & 91.04 & 91.45 & 68.89 & 92.56 \\ \hline FixMatch & 91.56 & 91.87 & 68.36 & 92.36 \\ \hline ours & 92.41 & 92.16 & 69.44 & 92.45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with different semi-supervised learning methods on ISIC 2018 Dataset.  
than the pure semi-supervised method and is more robust in the face of the fluctuation of labeled data.  
We also compare the specific performance of different methods in terms of AUC. As shown in Table 4, with 40% labeled data, the average AUC of our method reaches 94.12%, ahead of all four categories. This proves the effectiveness of our method. At the same time, we notice that the performance of each model shows a gap in different categories. This may be because labeling noise in datasets reduces the effect of consistency regularization and our unsupervised clustering shows better classification ability in this case. The training method  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Labeled**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{3-6}  & & **AUC** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline TCSE & 2\% & 84.12 & 84.36 & 63.14 & 85.21 \\ \hline  & 4\% & 85.25 & 86.12 & 64.21 & 86.31 \\ \hline TE & 2\% & 84.32 & 86.14 & 63.07 & 85.16 \\ \hline  & 4\% & 85.36 & 87.12 & 64.22 & 86.33 \\ \hline MT & 2\% & 87.23 & 89.75 & 63.89 & 89.24 \\ \hline  & 4\% & 88.45 & 90.11 & 65.14 & 90.13 \\ \hline UDA & 2\% & 89.36 & 91.07 & 66.19 & 91.14 \\ \hline  & 4\% & 90.71 & 91.34 & 67.33 & 91.79 \\ \hline FixMatch & 2\% & 90.13 & 91.03 & 66.24 & 91.45 \\ \hline  & 4\% & 90.69 & 91.25 & 67.39 & 92.07 \\ \hline ours & 2\% & 92.04 & 91.89 & 68.86 & 92.14 \\ \hline  & 4\% & 92.27 & 92.11 & 69.13 & 92.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different methods under different scale labeled data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Labeled**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{3-6}  & & **Error Rate** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{supervised} & 100\% & 21.23 & 81.25 & 81.69 & 81.46 \\  & 10\% & 36.24 & 59.41 & 56.36 & 57.84 \\ \hline \multirow{2}{*}{TCSE} & 10\% & 32.51 & 64.17 & 63.21 & 63.69 \\  & 20\% & 30.09 & 68  
of temporarily ignoring label information in the unsupervised training phase makes it less likely to overfit a small amount of training data.