# Self-Training with Entropy-Based Mixup for Low-Resource Chest X-ray Classification
## 1 Introduction  
Currently, research using machine learning technology in the field of medical image analysis is being actively studied [1; 2; 3; 4; 5]. Research on medical image analysis, particularly the development of deep learning-based systems for chest X-ray classification and diagnosis, has garnered increasing attention due to the growing importance of immediate and accurate diagnosis. These studies focus on leveraging deep learning technology to automate the annotation process, thereby facilitating efficient and accurate diagnosis [6]. However, since creating labeled data for medical image analysis requires experts and takes substantial time and money, obtaining medical image training data and dealing with imbalanced distribution of the training data are recognized as important problems. Especially in multilabel classification, learning with a small number of labeled data causes severe overfitting problems. The model easily overfits the limited number of labeled data, while it still underfits the large amount of unlabeled data. To address the challenges of imbalanced data distribution and overfitting caused by a limited number of labeled data, this study proposes a method that combines entropy-based Mixup [7; 8; 9; 10] and self-training [11; 12; 13; 14; 15]. The proposed approach involves generating new data samples by applying entropy-based Mixup to a small number of labeled data. In the Mixup process, data samples with high entropy and those with low entropy are selected for synthesis. By iteratively repeating this process, replacing the teacher model with the student model, and incorporating a self-training method that effectively utilizes a large volume of unlabeled data, optimal performance can be achieved. Mixup is a popular data augmentation technique used in this study. It combines two existing samples through a linear combination, eliminating the need for additional datacollection. The combination ratio is determined using a beta distribution. By generating these mixed samples, the model's generalization performance is improved by increasing the training data. Mixup introduces variability and enhances model robustness, even with challenging or unsuitable data. It effectively addresses overfitting by utilizing the principle of vicinal risk minimization (VRM). In multilabel classification tasks, Mixup proves valuable as it enables the model to capture complex label relationships. The learning model presented in this paper can be seen as a kind of self-training, as it generates pseudo-labels from unlabeled data that are used to retrain themselves to improve performance. In particular, the noisy student model combines the advantages of semi-supervised learning and supervised data augmentation to achieve improved results. In this study, we utilized the process of the teacher-student model and the concept of iterative training. The learning process in this study can be broadly divided into two main stages: training the teacher model using the entropy-based Mixup technique and self-training the student model with repetition. To provide a more detailed explanation, the process can be further divided into four steps. In the first step, we perform an entropy-based Mixup with a small number of labeled data to increase the number of samples and train the teacher model. In the second step, to extract meaningful samples from a large number of unlabeled data, the trained teacher model generates softmax prediction vectors of unlabeled data, classifies them by disease, and extracts the top-ranked data. In the third step, we use the extracted data samples to pretrain the student model and perform fine-tuning with the initial labeled data. In the final step, we select synthesized samples for data expansion, remove noisy synthesized samples that degrade performance, and repeat the process by using the student model as a teacher model. To confirm the validity of the proposed method, the classification performance of entropy-based data augmentation and self-training is verified using unbalanced chest X-ray data.