# Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning
###### Abstract  
In recent years, the application of federated learning to medical image classification has received much attention and achieved some results in the study of semi-supervised problems, but there are problems such as the lack of thorough study of labeled data, and serious model degradation in the case of small batches in the face of the data category imbalance problem. In this paper, we propose a federated learning method using a combination of regularization constraints and pseudo-label construction, where the federated learning framework consists of a central server and local clients containing only unlabeled data, and labeled data are passed from the central server to each local client to take part in semi-supervised training. We first extracted the class imbalance factors from the labeled data to participate in the training to achieve label constraints, and secondly fused the labeled data with the unlabeled data at the local client to construct augmented samples, looped through to generate pseudo-labels. The purpose of combining these two methods is to select fewer classes with higher probability, thus providing an effective solution to the class imbalance problem and improving the sensitivity of the network to unlabeled data. We experimentally validated our method on a publicly available medical image classification data set consisting of 10,015 images with small batches of data. Our method improved the AUC by 7.35% and the average class sensitivity by 1.34% compared to the state-of-the-art methods, which indicates that our method maintains a strong learning capability even with an unbalanced data set with fewer batches of trained models. federated learning; semi-supervised algorithm; pseudo-labels; classification +
Footnote †: journal: Medical Image Classification Based on  
To this end, we investigated an improved semi-supervised federated learning problem that introduces a treatment of the unlabeled-data class-imbalance problem based on the fact that only a small amount of labeled data is involved. To obtain useful information from unlabeled data, two methods are commonly used: One uses regularization to constrain unlabeled data using labeled data features [12; 14; 15; 16; 17], and the other uses a specific way to construct pseudo-labels [18; 19; 20] and set confidence thresholds to filter the predicted features. [PERSON] et al. [12] set a small amount of labeled data in a local client and achieved regularization constraints by extracting the intrinsic relationships of the categories in the labeled images in the form of a correlation matrix to laterally constrain the unlabeled data from other clients. Although further exploitation of labeled data is carried out, the training on unlabeled data only incorporates common consistent regularization methods to improve the robustness of the model and does not effectively address the problem of category imbalance at the data level. The difference is that [PERSON] et al. [21] used labeled data only as a model initialization training and focused their research on the training of local clients, where they applied the peer anonymous (PA) learning method to unlabeled clients, integrating similar clients to the community and obtaining pseudo-labeling of unlabeled data with the aid of peer learning. This method improved the training efficiency but still suffers from category imbalance at the data level. To solve this problem, [PERSON] et al. [18] used labeled data for pre-training and performed dynamic library building in a local client containing only unlabeled data. They transformed the original classification task into classification of the library by randomly setting different label ratios to avoid the problem of blurred decision boundaries in low categories caused by unbalanced data categories.