# Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning
## 4 Discussion  
We present a new federated learning method that can effectively solve the class imbalance problem in the melanoma dermoscopy image classification task. This is the first time that regularization constraints are combined with pseudo-labeled representations in semi-supervised federated learning, and this classification model demonstrates the improvement in the accuracy of the model using our approach by two validation methods. The classification model trained under our method has an accuracy of 95.75%, sensitivity of 72.92%, specificity of 95.47%, precision of 73.88%, F1 score of 72.90%, and AUC of 95.75%. In the process of proving the validity of our proposed method, we compared our method with the baseline level FedAvg [22] and two of the current state-of-the-art methods FedIRM [12] and imFed-Semi [18] in the field of federated learning. With the same data set, the same data distribution, the same batch, and the same model optimal performance, our method leads to the optimal level of the model. The sensitivity (recall), the most important metric in medical image classification, is improved by 5.17% compared to the state-of-the-art level, and the specificity, another important metric, is improved by 1.43%. In addition, we present a simple model-robustness evaluation criterion, which demonstrates the effectiveness of our proposed method in improving the robustness of the model by showing that our model yields the smallest prediction distribution error compared to the models trained by FedAvg [22], FedIRM [12] and imFed-Semi [18]. When we change the sample ratio between the medium and local clients by a controlled data sampler, as shown in Figure 9, we find that the sensitivity of important metrics is improved by 3.62% under the federated learning framework compared to the degraded ordinary semi-supervised method. This demonstrates the effectiveness of combining federated learning with semi-supervised learning for the improvement of classification performance of medical image classification models. Notably, by looking at Table 1, it can be observed that the accuracy index decreases by 5.24% compared to the state-of-the-art level, while Equation (14) reflects a significant decrease in accuracy when false positives (FP) rise. Through analysis, we believe that the reason is that the pseudo-labeling is constrained while relaxing the prediction of the few-category data labels, thus improving the sensitivity of the model to the few categories to some extent. In contrast to the decrease in sensitivity for most categories, described in Table 2, according to Equation (15) we observe a significant decrease in sensitivity when false negatives (FN) rise, due to the constraint of pseudo-label  
Figure 9: The sensitivity variation of our method with the same amount of data and the number of clients in different places. prediction of most category data labels. In short, this phenomenon occurs uniformly, i.e., all images from the majority category are predicted into the minority category, but we believe that this phenomenon can be tolerated, because from a theoretical point of view, the sensitivity of the minority category is substantially improved, which nicely solves the category imbalance problem. From a practical point of view, we believe that a larger number of image categories corresponds to diseases with higher probability of occurring. This means, there are relatively more experience and methods to deal with that category of diseases in clinical treatment, and doctors have higher recognition of that category of diseases. This is a deficiency that can be largely compensated for manually, while a few categories of diseases lack the above elements, so we believe that the classification model trained by our method has more advantages than disadvantages. In addition to these shortcomings of our approach, in terms of model initialization only simple supervised training is used, and we believe that such a migration of the parameter distribution is not suitable for semi-supervised learning.