# Semi-Supervised Medical Image Classification Combined with Unsupervised Deep Clustering
## 1 Introduction  
With the development of deep learning technology, deep neural networks have become widely used in medical image analysis and processing. However, due to the need for professional knowledge, it is difficult to manually label medical images on a large scale. At the same time, there are constantly unlabeled data being generated in the clinical medical process, which results in a small number of labeled images and a large number of unlabeled images being generated. In this case, semi-supervised learning [1, 2], which can use both labeled data and unlabeled data, has certain advantages. In this paper, we propose a semi-supervised learning framework combined with unsupervised learning [3, 4, 5], which can enhance the model's ability to extract information from unlabeled data, reduce the risk of overfitting, and effectively reduce the impact of outlier data. Slight disturbances in an image do not affect a human's basic judgment of the image's semantics. Based on this feature, people apply the consistency regularization method [6, 7, 8] to semi-supervised learning, and generate different samples by adding different disturbances (such as Gaussian noise, flipping, cropping, etc.) to the input samples, and then encourage the model to generate similar predictions for the different disturbed versions of the same input. Theoretically, a good model can eliminate the influence of disturbance and make self-consistent predictions. For example, in the \(\pi\)-model [6], for any input \(x_{j}\), the model generates two different disturbances and inputs them into the feature extraction network to obtain two predictions. The differences between these predictions are thenused as the objective function to optimize the model. The TE model [6] takes the EMA (exponential moving average) predictions in different epochs as the comparison target. The MT (mean teacher) model [7] has two parallel feature extraction networks, and different disturbance samples are input into different networks, in which the teacher network uses the EMA weight of the student network, and the output results of the two networks are regarded as the consistency target. However, the current semi-supervised method also has some limitations. For example, the unsupervised phase of the model depends on the label information of supervised learning, and it is difficult to avoid over-fitting when the scale of labeled data is small. For example, most semi-supervised methods assume that labeled data and unlabeled data obey the same distribution and have the same label space, which makes the occasional outlier in medical images (including instrument failure, improper operation, diseases that are not within the prediction range, etc.) often have negative effects on the quality of prediction. In this paper, we introduce an unsupervised learning process that is completely independent of semi-supervised learning, which ensures that this part of the training is not affected by supervised learning, so we can produce pseudo-labels [9] that are not affected by label information. As shown in Figure 1, we represent the model as two phases: a semi-supervised phase and an unsupervised phase, which share the feature extraction network. Among them, the semi-supervised phase is the main task to perform the image classification task, and the unsupervised phase is the secondary task to cluster the data [10; 11; 12; 13]. Our semi-supervised network architecture is based on the basic architecture of FixMatch [14]. Our work focuses on the unsupervised clustering phase. In order to prevent cluster degradation in the iterative process, we design a network structure similar to a Siamese Network and update the weights by momentum [15]. At the same time, the auxiliary overclustering method [16] is adopted to make the outliers less likely to affect the clustering results. Our main contributions are as follows:  
1.