# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Hybrid Methods  
This section covers a few models which combine two or more of the previously reviewed approaches. Local and Global Consistency Regularized Method [77]: This method is both consistency-based and graph-based. This method uses the Mean Teacher framework and also enforces the local and global consistency of the data [78]. Local consistency is the tendency of instances from the same class to be in the same area in the feature space, and global consistency is the tendency of instances from the same global structure to have the same label. This \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Type** & **Dataset Used** & **Best Results** \\ \hline \multirow{3}{*}{ACPL [37]} & \multirow{3}{*}{Pseudo-labeling} & Chest X-ray14 and & Chest X-ray: AUC 81.77 \\  & & ISIC2018 Skin Lesion & ISIC2018: AUC 94.36, \\  & & Analysis & F1 62.23 \\ \hline \multirow{3}{*}{FL with IRM [74]} & \multirow{3}{*}{Federated learning} & RSNA Brain CT and & RSNA: AUC 87.56, F1 59.86 \\  & & ISIC2018 Skin Lesion & ISIC2018: AUC 92.46, F1 \\  & & Analysis & 55.81 \\ \hline \multirow{3}{*}{Semi-HIC [76]} & \multirow{3}{*}{Learning by association} & Bioimaging2015 and & Bioimaging2015: error \\  & & BACH & rate 25.23 \\ \cline{1-1} \cline{3-3}  & & BACH: error rate 15.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of other methods. method integrates label propagation (LP) to enforce local and global consistency. LP is a semi-supervised learning algorithm that involves the propagation of labels from labeled samples to unlabeled samples based on their closeness, which is determined by the affinity matrix. For an unlabeled instance \(x\), the label is calculated by taking the weighted average of the labeled instances close to \(x\); then, the new label of \(x\) can be propagated to other neighboring unlabeled data. Next, a graph is constructed based on the labels created by the LP algorithm as well as the ground truth labels:  
\[A_{ij}= \begin{cases}1,&\text{ if }y_{i}=y_{j}\\ 0,&\text{ otherwise}\end{cases}  \tag{34}\]  
in which \(y_{i}\) and \(y_{j}\) represent the labels of the data. Contrastive Siamese loss [79] is used in order to enforce local and global consistencies by pulling the instances from the same class closer and pushing the ones from different classes farther apart:  
\[L_{s}= \begin{cases}\parallel z_{i}-z_{j}\parallel^{2},&\text{ if }A_{ij}=1\\ max\Big{(}0,m-\parallel z_{i}-z_{j}\parallel^{2}\Big{)},&\text{ if }A_{ij}=0\end{cases}  \tag{35}\]  
where \(m\) is a hyperparameter and \(z\) is the feature vector extracted from the intermediate layers of the student network. The final loss function is as follows:  
\[L_{\text{total}}=\text{Loss}_{mt}+w(\tau)\Bigg{(}\lambda_{g1}\sum_{x_{i},x_{j} \in X_{l}}L_{s1}+\lambda_{g2}\sum_{x_{l}\in X_{l},x_{l}\in X_{u}}L_{s2}\Bigg{)} \tag{36}\]  
where the total loss is a weighted addition of the Mean Teacher loss (\(\text{Loss}_{mt}\)) and the graph-based loss (\(L_{s1}\) and \(L_{s2}\)). \(\lambda_{g1}\) is the weight of the loss calculated on the labeled instances and \(\lambda_{g2}\) is the weight of the loss calculated on both labeled and unlabeled instances. The loss on the unlabeled instances is not calculated separately as the labels predicted by the LP algorithm are noisy, and their inclusion may harm the performance of the method. Testing was conducted using two datasets, the Multi-organ Nucleus Segmentation (MoNuSeg) dataset containing 22,462 samples and the Ki-67 nucleus dataset containing 17,516 samples. In both datasets, each sample is labeled with one of four types, leading to a single labeled classification task. For both datasets, 80% of the data was used for training and 20% for testing. From the training data, 20% was used for validation. The model architecture is a 13-layer convolutional neural network similar to the one proposed in the original Mean Teacher method [27]. The metric used to gauge the method's performance is F1.