# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Hybrid Methods
Pseudo-labeling generative adversarial networks (PLGAN) [81]: This model proposed by [PERSON] et al. [81] is based on pseudo-labeling and GANs. It additionally incorporates CL and MixMatch. The training process of this method is comprised of 4 steps; (1) pretraining, (2) image generation, (3) finetuning, and (4) pseudo-labeling. In step 1, the feature layer of ResNet50 [59] is pre-trained using CL to extract key image features. In step 2, GANs are used to generate images that simulate the real distribution of the labeled images with random Gaussian noise. In step 3, the cross-entropy loss is used to classify the images created by the generator in order to finetune the discriminator for classification. The model contains two classifiers, a global and a local classifier, both of which use two convolutional blocks to extract feature information. In step 4, the MixMatch approach is used for pseudo-labeling. The trained generator is used to create additional unlabeled samples in order to expand the unlabeled dataset, and these generated images are combined with the original dataset. In the same fashion, the pseudo-labels created for both the original and generated samples are combined to create the complete set of pseudo-labels. The overall loss function of the model contains four loss functions for each of the 4 steps. The loss function for the first step is the infoNCE [82] loss for CL and the reconstruction loss for intermediate vectors to avoid CL pattern collapse. For the second step, the loss function is the least squares loss. The loss of the semi-supervised finetuning step is the addition of the supervised cross-entropy loss and the unsupervised loss. Lastly, the fourth step uses the loss function of MixMatch. The model was tested on a dataset of optical coherence tomography (OCT) images for the classification of retinal degeneration, with each sample belonging to one of four categories (three disease labels and one normal label). Additionally, it was tested on a COVID-19 dataset, a brain tumor MRI dataset, and a chest X-ray dataset. For the OCT and chest X-ray datasets, 100 labeled and 1000 unlabeled images were used. In the MRI dataset, 100 labeled images and 480 unlabeled images were selected. Lastly, 200 labeled and 200 unlabeled images were used for the COVID-19 dataset. The results achieved by the model were classification accuracies of 87.06, 89.50%, 80.50%, and 96.80% for the OCT, COVID-19, MRI, and X-ray datasets, respectively. In addition to PLGAAN, the authors developed PLGAAN+, which uses Comatch [45] for pseudo-labeling. This model achieved accuracy scores of 97.10%, 89.30%, 86.80%, and 97.50% for the OCT, COVID-19, MRI, and X-ray datasets, respectively. These results were compared to the results gained by several other state-of-the-art SSL models, including Comatch, MixMatch, FixMatch, and VAT. Overall, PLGAAN+ achieved more favorable results than all other models. Semi-supervised medical image classification based on CamMix [83]: [PERSON] et al. [83] proposed an SSL model for medical image classification similar to MixMatch, which combines several SSL approaches. The consistency-based approach is used on unlabeled data to generate pseudo-labels that are robust to various augmentations. The authors argue that the MixUp method, which mixes samples by linearly interpolating the input samples and labels, results in unnatural mixed samples. Thus, they proposed CamMix, a novel MSDA method that mixes pairs of input samples and labels based on the class activation mask generated from the predictions of both labeled and unlabeled samples. As in MixMatch, entropy minimization is achieved by sharpening the target distribution for unlabeled data. The class activation map is obtained as follows for each batch \(b\) at each epoch:  
\[GradMaxCam^{b}=max\Big{(}ReLU\Big{(}\sum_{k}w_{k}^{b}A^{k}\Big{)}\Big{)} \tag{38}\]  
where \(w_{k}^{b}\) is the weight of feature map \(k\) for batch \(b\) and \(A\).