# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Graph-Based Methods
 \tag{25}\]  
Based on the label information for unlabeled samples gained from the graph embedding, the new information gained is as follows:  
\[G_{m}(w,\tau,X_{l},Y_{l},X_{u})= \tag{26}\] \[G_{m}(S)-(|S_{l}|G_{m}(S_{l})+|S_{u}|G_{m}(S_{u}))/|S|\]  
where \(S\) is the node, \(S_{l}\) is the left child node, \(S_{u}\) is the right child node, \(\tau\) is a threshold, \(X_{l}\) and \(Y_{l}\) represent labeled instances and the class labels, respectively, and \(X_{u}\) represents unlabeled instances. The medical image datasets used for the classification task were the Digital Retinal Images for Vessel Extraction (DRIVE) 2D retinal vessel dataset and the Big Neuron 3D neuronal dataset. For both datasets, 40,000 samples were randomly selected from the training and testing sets, respectively, each containing 20,000 positive and 20,000 negative samples. The results were compared with the standard random forest trained on labeled data only, as well as the RobustNode method [71] trained on both labeled and unlabeled data. The results showed considerable improvement over both the standard random forest and the RobustNode method. For the DRIVE dataset, training with a labeled dataset size of 400 achieved an accuracy of 79.42%. For the Big Neuron dataset, training with 1500 labeled data achieved an accuracy of 74.16%. Colorectal Cancer Tissue Classification Using Semi-Supervised Hypergraph Convolutional Network [72]: [PERSON] et al. [72] propose a hypergraph-based approach for colorectal cancer (CRC) classification. Hypergraphs allow more complex relationships between nodes compared to standard graphs by allowing one edge to join multiple nodes. The images used for classification were CRC Whole Slide Images (WSIs), high-resolution images captured from a microscope slide representing tissue structures that can be used to identify malignancy. First, patches of size 224 \(\times\) 224 are extracted from the images, and a VGG-19 [73] model in feed-forward mode is used to extract the feature matrix \(X\) from the set of \(n\) patches. The hypergraph \(\mathcal{G}(V,E,W)\) is then constructed from \(X\) by connecting each vertex with its \(k\) nearest neighbors. The hypergraph is represented by a vertex-edge probabilistic incidence matrix \(H\) of size \(n\times n\) as follows:  
\[h(n,e)=\left\{ \begin{array}{ll}exp\Big{(}\frac{-d}{p_{max}d_{\text{avg}}} \Big{)},&\text{if }n\in e\\ 0,&\text{if }n\ \
otin e\end{array} \right. \tag{27}\]  
where \(d\) is the Euclidean distance between the current node and the neighbor node, \(d_{\text{avg}}\) is the average Euclidean distance between the \(k\) neighbors, and \(p_{max}\) is the maximum probability. The degrees of each vertex \(v\in V\) and edge \(e\in E\) were calculated as follows:  
\[d(v)=\sum_{v\in V}h(v,e),d(e)=\sum_{e\in E}h(v,e) \tag{28}\]  
The combination of all node and edge degrees results in the diagonal matrices \(D_{v}\) and \(D_{e}\). In the classification step, \(X\) and \(H\) are fed to a hypergraph neural network (HGNN) consisting of three hidden convolutional layers and a SoftMax classification layer. Spectral graph convolution is used for representation learning as follows:  
\[X^{L+1}=\sigma\Big{(}D_{v}^{-1/2}HWD_{e}^{-1}H^{T}D_{v}^{-1/2}X^{L}\theta^{L} \Big{)} \tag{29}\]where \(\sigma\) is the activation function, \(\theta\) is a parameter to be learned during training, and \(W\) is one's diagonal matrix. Layer \(L\) outputs \(X^{L+1}\) which is received as an input by layer \(L+1\). The model was tested on a dataset of CRC WSIs containing 4995 patches of size 224 \(\times\) 224 representing 7 different tissue types. 70% of the dataset was used for training and 30% for testing. The model achieved an F1 score of 0.94 for the seven-class classification task. A summary of the methods discussed in this section is provided in Table 3. Three graph-based methods have been reviewed: GraphXNET, Graph-Embedded RF, and CRC with HGNN. For each model, the table contains the datasets used for testing as well as the best performance score obtained on each dataset.