# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 2 Background
### Other Methods  
In addition to the three main categories, we also review papers implementing methods that are less common in the recent literature. For example, one such method is pseudo-labeling, where a supervised learning model is trained on the labeled data and then used to classify a part of the unlabeled data. The pseudo-labels generated with high confidence are then added to the labeled data, and the model has trained again [37]. Traditional pseudo-labeling methods have several disadvantages, such as their tendency to be biased toward the majority class and their inability to be seamlessly adapted to multi-label and multi-class problems. Confidence-based pseudo-labeling leads to the selection of samples from the majority class as they tend to have higher confidence, resulting in a majority-biased classification model [37]. As such, pseudo-labeling methods are usually used in conjunction with consistency-based, adversarial, or graph-based methods in order to mitigate these disadvantages. In addition to pseudo-labeling, papers addressing Federated Learning, Learning by Association, Contrastive Learning, and MixMatch will also be reviewed. Federated Learning (FL) is a decentralized system that allows training to be conducted on distributed data sources such as different hospitals separately. The parameters of the locally trained models can be used to update the main algorithm without exchanging private information, making FL an appropriate privacy-reserving method of data collaboration between different institutions. In semi-supervised FL, each of the local datasets can be labeled or unlabeled [38]. The Learning by Association (LA) [39] model assumes that good embeddings that belong to the same class will have high similarity. It builds association cycles through a \"walk\" from the embedding vector A obtained from a batch of labeled data and embedding vector B obtained from a batch of unlabeled data. The \"walker\" goes from A to B and then back to A based on mutual similarities. If walker ends up in the same class that it started from, the cycle is considered correct. Thus, the goal is to maximize the possibility of a correct association cycle. The \"walker\" loss is defined as:  
\[L_{\text{walker}}\ =H\left(T,P^{\text{sub}}\right) \tag{7}\]  
with \(H\) representing the cross-entropy between the association cycle probabilities \(P^{\text{sub}}\) and the uniform target distribution of correct association cycles \(T\). Additionally, [PERSON] et al. [39] argue that instead of making associations only among \"easy\" samples, it is beneficial to \"_visit_\" all unlabeled samples in order to make the best use of the dataset and encourage embeddingsthat generalize better. Thus, the visit loss \(L_{visit}\) is proposed, which is the cross-entropy between the visit probabilities \(P^{visit}\) and the uniform target distribution \(V\):  
\[L_{visit}=H\Big{(}V,P^{visit}\Big{)} \tag{8}\]  
Contrastive Learning (CL) [40] is a form of self-supervised learning which involves generating augmentations of datapoints to learn the higher-level features of a dataset from unlabeled samples. The augmentations of the same datapoint are considered positive samples and encouraged to be close to each other, while the augmentations of different datapoints are considered negative samples and encouraged to be far apart. The goal of this process is to create a model with higher generalization capability across different data distributions. Joint Contrastive Learning (JCL) [41] is a form of CL in which an infinite number of key-query pairs are generated from unlabeled data. As opposed to each key-query pair being compared independently as in the original CL method, multiple key-query pairs containing positive keys paired with the same shared query \(q\) are encouraged to be close. This leads to more consistent representations in each instance-specific class. MixMatch is a recent SSL method proposed by [PERSON] et al.