# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 5 Summary of Results  
Table 6 shows a comparison of the models which were tested on the Chest X-ray14 dataset for multi-label classification and/or the ISIC2018 for multi-class classification. For both datasets, all models used 20% of the data as the labeled dataset and 80% as the unlabeled dataset. Among them, the highest score on the Chest X-ray14 dataset was achieved by the S\({}^{2}\)MTS\({}^{2}\) method (AUC of 82.51), followed by ACPL (AUC of 81.77). On the ISIC2018 dataset, the highest score was achieved by Uncertainty-guided VAT with BNN with an F1 score of 69.67 and AUC of 96.04, considerably higher than all other models. It is worth noting that ACPL obtained relatively high scores on both datasets, showing that pseudo-labeling can indeed achieve state-of-the-art results by improving the measure used for the selection of informative pseudo-labels.  
Figure 3 showcases the results obtained by different methods for each dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Type**} & **Chest X-ray14** & **ISIC2018** \\ \cline{3-5}  & & **AUC** & **AUC** & **F1** \\ \hline SRC-MT [19] & Consistency-based & 79.23 & 93.58 & 60.68 \\ Not [52] & Consistency-based & 79.49 & – & - \\ SFNTS\({}^{2}\)[55] & Consistency-based & 82.51 & 94.71 & 62.67 \\ Uncertainty-guided VAT & & & & \\ with BNN [56] & Adversarial (VAT-based) & 80.69 & 96.04 & 69.67 \\ GraphX\({}^{2}\)[34] & Graph-based & 78.00 & - & - \\ ACPL [37] & Pseudo-labeling & 81.77 & 94.36 & 62.23 \\ FL with IRM [74] & Federated learning & - & 92.46 & 55.81 \\ SSL with CamMix [83] & Consistency-based + CamMix & - & 94.04 & - \\  & & (MSDA) & - & 94.04 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of methods tested on the Chest X-ray14 and ISIC2018 datasets, at 20% labeled data for all models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Type** & **Dataset Used** & **Best Results** \\ \hline Local and Global Consistency Regularized [77] & Consistency-based + Graph-based & MoNuSeg and Ki-67 & MoNuSeg: F17.68 \\ \hline [PERSON] et al. [80] & Consistency-based + Adversarial (VAT) & Breast Ultrasound (BRUS) and OCT & BRUS: F1 0.88 \\ \hline PLGAN [81] & Pseudo-labeling + Adversarial (GANs) & OCT, COVID-19, Brain Tumor MRI, Chest X-ray & OCT: 97.10 \\  & & & \\  & & & \\  & & & \\ \hline SSL with CamMix [83] & Consistency-based + CamMix (MSDA) & ILD dataset, ISIC2018 & ILD: AUC 95.34 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of hybrid methods.  
Among the GAN-based methods, the bi-modality image synthesis model is notable as it allows a more comprehensive look at a certain medical problem by combining different types of scans of the same area, leading to a more successful diagnosis. Despite the usefulness of bi-modality and multi-modality medical image diagnosis, labeled data in this category is even more scarce, making the semi-supervised image synthesis method to create more labeled inputs a promising method.  
Hybrid models which use a combination of different SSL approaches are popular in recent research and show much promise. Most of these models incorporate consistency regularization, including the MixMatch method, which has gained considerable attention. Hybrid models inspired by or incorporating MixMatch, as well as DSNA approaches such as MixUp, are common. Among the hybrid methods, the consistency-based adversarial method proposed by [PERSON] et al. achieved impressive results with a labeled dataset of only 691 images. However, this study assumes that the labeled and unlabeled data have the same distribution. Therefore, further experimentation with differently distributed data is needed.  
Figure 3: The performance of the methods on all datasets. The best result for each dataset is bolded.