# Semi-Supervised Medical Image Classification Based on Attention and Intrinsic Features of Samples
## 4 Experiments
### ISIC 2018 Dataset  
The ISIC 2018 dataset is used for single-label classification. It consists of 10,015 skin lesion examination images, which are labeled as instances of seven common skin lesions. We divide the dataset as follows: 70% for training, 10% for verification, and 20% for testing. There are 60 rounds of training, and the ramp factor T is set to 30. Before training, we adjust each image in the dataset to a size of 224 \(\times\) 224 and normalize it using the statistical data collected from the ImageNet dataset. We use DenseNet121 [4], which is pre-trained on ImageNet, as the backbone of our network, and use AUC--Accuracy, Sensitivity, and Specificity--to evaluate our model.  
Table 1 shows the classification performance of the latest semi-supervised framework with 20% of the labeled data (1400 images). As we can see, the self-training method has more advantages than other methods in terms of Specificity, while its indicators need to be improved. While SS-DCGAN improves AUC by 0.7%, which indicates that the GAN-based method helps to improve the semi-supervised classification framework. On the other hand, the TCSE method indexes show that consistency regularization is effective in utilizing unlabeled data. However, TE integrates the forecasts of different periods and generates a more reliable consistency target, so it performs slightly better than TCSE. The performance of MT AUC proves its superiority in semi-supervised learning. SRC-MT improves four indicators at the same time, which proves that SRC can make full use of unlabeled data information. The AUC and Sensitivity indicators for our method show that it helps the model to make more effective use of unlabeled data and improve the classification of positive samples, while the improved performance of negative samples is poor.  
We also studied the influence of the different percentages of labeled data, as shown in Table 2: under the settings of 5%, 10%, and 20% labeled data, our method achieved 94.02% AUC and 72.03% Sensitivity, but the results were not clear for the other two indicators. These results demonstrate the impact of our method with different labeling ratios for the data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{2-5}  & **AUC** & **Sensitivity** & **Accuracy** & **Specificity** \\ \hline Self-training [8] & 90.58 & 67.63 & 92.37 & 93.31 \\ SS-DCGAN [14] & 91.28 & 67.72 & 92.27 & 92.56 \\ TCSE [17] & 92.24 & 68.17 & 92.35 & 92.51 \\ TE [30] & 92.70 & 69.81 & 92.26 & 92.55 \\ MT [18] & 92.96 & 69.75 & 92.48 & 92.20 \\ SRC-MT [39] & 93.58 & 71.47 & 92.54 & 92.72 \\ \hline Ours & 94.02 & 72.03 & 92.61 & 91.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison between the ISIC 2018 dataset and previous examples of semi-supervised learning.  
Figure 4 shows the AUC indicators of our method for each category of the ISIC 2018 dataset, in which the AUC performance of our method reaches 95% in most categories, and the worst performance in Melanoa and Benign keratosis, which may be due to the small number of inter-class characteristics making it difficult for the model to accurately identify it.