# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 2 Background
### Other Methods
[42] that aims to unify three different loss terms, such as entropy minimization loss, which encourages the generation of labels with high confidence. The consistency regularization loss encourages output consistency in the presence of perturbations, and the generic regularization loss encourages better generalization. Consistency regularization is achieved by augmenting both labeled and unlabeled images with standard augmentation techniques (random horizontal flips and crops for images) and enforcing consistency in their results. Entropy minimization is achieved by sharpening the target distribution for unlabeled data. Lastly, traditional regularization is applied using a weight decay that penalizes the \(L_{2}\) norm of the model parameters. Additionally, the Mixed Sample Data Augmentation (MSDA) method MixUp [43] is used for both regularization and semi-supervised learning. MSDA methods involve mixing different samples and their labels based on a particular policy to create an augmented dataset. The MixUp approach involves training the model on convex combinations of pairs of images and their labels to encourage linear behavior in-between training samples. Thus, the algorithm works as follows. A batch of labeled data \(X\) and a batch of unlabeled data \(U\) is fed to the model, and augmentations are applied to both. The label predictions of all augmentations of \(U\) are computed and averaged, and sharpening is applied to the average prediction. The augmented labeled dataset \(\hat{X}\) and the augmented unlabeled dataset with guessed labels \(\hat{U}\) are combined and shuffled to obtain set W. Then, MixUp is applied to the labeled dataset \(\hat{X}\) and entries from W to obtain the processed labeled dataset \(X^{\prime}\); in the same way, MixUp is applied to the unlabeled dataset \(\hat{U}\) and the rest of W to obtain the processed unlabeled dataset \(U^{\prime}\). After the processed labeled and unlabeled (with guessed labels) batches \(X^{\prime}\) and \(U^{\prime}\) are obtained, they are used for semi-supervised training with the loss function \(\mathcal{L}_{X}+\lambda_{\mathcal{U}}\mathcal{L}_{U}\) which combines the supervised loss \(\mathcal{L}_{X}\) and the unsupervised loss \(\mathcal{L}_{U}\) with the hyperparameter \(\lambda_{\mathcal{U}}\). \(\mathcal{L}_{X}\) is the cross-entropy loss obtained from training on \(X^{\prime}\) and \(\mathcal{L}_{U}\) is the squared \(L_{2}\) loss obtained from training on \(U^{\prime}\). FixMatch [44] is an SSL method similar to MixMatch that combines different existing SSL approaches, including consistency regularization and pseudo-labeling. FixMatch encourages label consistency between the pseudo-label of a weakly-augmented version of an input image and the pseudo-label of a strongly augmented version of the same image. Comatch [45] is another \"SSL method that combines entropy minimization, consistency regularization, contrastive learning, and graph-based SSL. In this method, the training data is represented by a class probability and a low-dimensional embedding which evolve in a co-training framework. A smoothness constraint is applied to the class probabilities to improve pseudo-labels which are then used to train the classification head and projection head with a cross-entropy loss and graph-based contrastive loss respectively.