# Self-Training with Entropy-Based Mixup for Low-Resource Chest X-ray Classification
## 5 Conclusions  
In this study, we proposed a model that combines entropy-based Mixup and self-training to improve the performance of chest X-ray multilabel classification by alleviating the problem of data shortage and imbalance distribution.  
The data augmentation by entropy-based Mixup efficiently increased the number of data samples to overcome the overfitting problem caused by severe imbalance in training dataset. The self-training process effectively utilized unlabeled data to improve the generalization performance of the model; by repeating the teacher-student cycles, the performance was further improved as it collected more high-quality training data.  
The experimental results on the ChestX-ray14 dataset showed that the proposed entropy-based Mixup could increase the AUROC average by 1.8-2.7%, and the self-training further increased the AUROC average by 8.2%. It was also shown that, as the  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & **Teacher Model** & **Student Model** & **Student Model** & **Student Model** & **Student Model** \\  & **(_n_ = 0)** & **(_n_ = 1)** & **(_n_ = 2)** & **(_n_ = 3)** \\ \hline AUROC average & 0.6522 & 0.7059 & 0.7221 & 0.7409 \\ \hline Improvement & - & +8.2\% & +2.3\% & +2.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of repeated self-training (\(P=10\), \(K=2000\)).  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \(P\) & \(K\) & **AUROC Avg.** \\ \hline \(P=5\) & & 0.6541 \\ \hline \(P=10\) & \(K=2000\) (fix) & **0.7059** \\ \hline \(P=14\) & & 0.6771 \\ \hline \multirow{3}{*}{\(P=10\)(Fix)} & \(K=1000\) & 0.6450 \\ \cline{2-3}  & \(K=2000\) & **0.7059** \\ \cline{2-3}  & \(K=4000\) & 0.6615 \\ \hline \hline \end{tabular}
\end{table}
Table 3: AUROC of student model for various \(P\), \(K\) combinations.  
repeated self-training cycle progressed, the AUROC average increased continuously to the third iteration.  
As a follow-up study, we plan to further develop the proposed method by applying label guessing and label smoothing [33] to the entropy-based Mixup algorithm for more effective utilization of unlabeled data. We also plan to develop a method for dynamically adjusting the parameters in self-training while iterating, as well as apply the RandAugment [11; 25] technique to the self-training for data expansion.  
Writing--original draft, M.P.; Writing--review & editing, J.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A2C2008414) and by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2020-0-01789) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation).  
Not applicable.  
We utilized the publicly available CXR8 dataset for experimentation. The dataset can be accessed online at the following link: [[https://nihcc.app.box.com/v/ChestXray-NIHCC](https://nihcc.app.box.com/v/ChestXray-NIHCC)]([https://nihcc.app.box.com/v/ChestXray-NIHCC](https://nihcc.app.box.com/v/ChestXray-NIHCC)) (accessed on 1 March 2021).  
The authors declare no conflict of interest.