# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Other Methods  
The following section provides reviews of methods that do not neatly fit into the previous three categories. Anti-curriculum Pseudo-labeling for Semi-supervised Medical Image Classification (ACPL) [37]: [PERSON] et al. [37] propose a pseudo-labeling based image classification method for the Chest X-ray14 and ISIC2018 Skin Lesion Analysis datasets. The ACPL approach aims to address the disadvantages of traditional pseudo-labeling and develop a model capable of achieving state-of-the-art results on par with consistency-based methods. ACPL relies on the existence of a distribution shift between labeled and unlabeled data. The unlabeled samples chosen for pseudo-labeling are the ones located as far as possible from the labeled data's distribution, making them more likely to belong to the minority class and, thus, more informative, resulting in a more balanced training process. The informativeness of a sample is determined using the cross-distribution sample informativeness (CDSI) measure, which computes the closeness of the unlabeled sample to the set of most informative labeled samples called the anchor set (\(\mathcal{D}_{A}\)). The CDSI was calculated as follows:  
\[h(f_{\theta}(x),\mathcal{D}_{A})=\left\{ \begin{array}{ll}1,&p_{\gamma}(\zeta =high|x,\mathcal{D}_{A})>\tau\\ 0,&\text{otherwise}\end{array} \right. \tag{30}\]  
in which \(\zeta\) denotes the information content random variable (low, medium, high), \(\gamma\) represents the parameters of the Gaussian Mixture Model (GMM) and \(\tau=max\{p_{\gamma}(\zeta=\text{ low }|x,\mathcal{D}_{A}),\)\(p_{\gamma}(\zeta=\text{ medium }|x,\mathcal{D}_{A})\}\). Once the set of most informative unlabeled samples is selected, the informative mixup (IM) method is used for pseudo-labeling. This method mixes the labels obtained from the K-nearest neighbor (KNN) classification with the labels obtained from the model \(p_{\theta}(. )\), where \(p_{\theta}(x)=\sigma(f_{\theta}(x))\) wherein \(f_{\theta}(x)\) is the input image feature and \(\sigma(. )\) is the final activation function to calculate an output in \([0,1]^{|\mathcal{Y}|}\). The IM method carries out the pseudo-labeling process by calculating a linear combination of the model prediction \(p_{\theta}(x)\) and the KNN prediction is weighted by the density score. Following the pseudo-labeling stage, the Anchor Set Purification (ASP) algorithm is used to select the most informative pseudo-labeled samples to be added to the anchor set. The backbone model used for experimentation was DenseNet-121 [23] for both datasets. The images in the X-ray14 dataset were resized to 512 \(\times\) 512. The training was performed with batch size 16 and a learning rate of 0.05 for 20 epochs in the first step and then 50 epochs, where the anchor set was updated with new pseudo-labeled samples every 10 epochs. The model achieved AUC scores of 74.69, 79.96, 79.90, 80.31, and 81.06 for labeled dataset sizes of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Type** & **Dataset Used** & **Best Results** \\ \hline GraphXNET [34] & Graph-based & Chest X-ray14 & AUC 0.78 \\ \hline \multirow{3}{*}{Graph-Embedded RF [70]} & \multirow{3}{*}{Graph-based} & DRIVE 2D retinal vessel and Big Neuron 3D neuronal & DRIVE: accuracy 79.42 \\  & & & Big Neuron: accuracy \\ \cline{1-1}  & & Neuron 3D neuronal & 74.16 \\ \hline CRC with HGNN [72] & Graph-based & CRC WSI dataset & F1 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of graph-based methods. 2%, 5%, 10%, 15%, and 20%, respectively. For the ISIC2018 dataset, images were resized to 224 \(\times\) 224. The training was conducted with batch size 32 and a learning rate of 0.001 for 40 epochs in the warm-up stage, followed by 100 epochs, with the anchor set being updated with ASP every 20 epochs. The model achieved AUC and F1-scores of 94.36 and 62.23 at a labeled set size of 20%. Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching [74]: [PERSON] et al. [74] propose an SSL method based on FL.