# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Adversarial Methods
In the supervised training, the original paired images are provided, and so for each pair of fake images generated by the generator, the corresponding true pair in the original data can be found. Thus, the loss function of the supervised training is the pixel-wise reconstruction loss:  
\[L_{1}=\mathbb{E}_{I^{1},I^{2}\sim p(I^{1},I^{2})}\Big{[}\parallel I^{1}-\hat{I }^{1}\parallel+\parallel I^{2}-\hat{I}^{2}\parallel\Big{]} \tag{22}\]  
where \(\hat{I}^{1}\) and \(\hat{I}^{2}\) represent the fake images while \(I^{1}\) and \(I^{2}\) represent the real images and \(\parallel x-\hat{x}\parallel\) represents the average Manhattan-distance calculated pixel-wise between intensities of images \(x\) and \(\hat{x}\). In order to counter a severe overfitting issue present in the supervised learning model due to the small number of labeled images available, an unsupervised learning model is also deployed. In the unsupervised model, the generator is trained using unpaired images and noise vectors rather than encodings. The model aims to minimize the Wasserstein (\(W\)) distances between the fake and real images. As such, the loss function of the unsupervised generator is:  
\[L_{unsup}=W^{1}+W^{2} \tag{23}\]  
where \(W^{1}\) is the \(W\) distance between the real and fake images of modality 1 and \(W^{2}\) is the \(W\) distance between the real and fake images of modality 2. The semi-supervised training is carried out as follows: in one iteration, the generator is trained in a semi-supervised manner with a set of paired training images. In the next iteration, the decoder and image translator are trained in an unsupervised manner using a set of unpaired images. The supervised and unsupervised training are alternated for 40,000 iterations. The usage of supervised learning allows for the creation of correctly paired images, while unsupervised training allows for higher realism and diversity. The images generated by this model were used as real training data in a single-label prostate cancer classification task in which each pair of images is classified as clinically significant (CS) or non-clinically significant (nonCS). The classifier was trained using 483 synthetic multimodal images of type ADC-T2w, and the testing set contained 50 real CS images and 50 real nonCS images. The evaluation metrics used were the Inception Score (IS) and Frechet Inception Distance (FID) for quality assessment of the synthetic images, Mutual Information Distance (MID) to assess the correctness of the pairings between ADC and T2w synthetic images, and finally, the accuracy of the classification task. When compared with four state-of-the-arts GAN-based image synthesis methods ([PERSON] et al. [64], CoGAN [65], CycleGAN [66], and pix2 pix [67]), the proposed model achieved better results in all metrics, with a classification accuracy of 93%. A summary of the methods discussed in this section is provided in Table 2. Three adversarial methods have been reviewed: Uncertainty-guided VAT with BNN, SSAC, and Bi-Modality Image Synthesis. For each model, the table contains the specific adversarial approach (VAT-based or GAN-based), the datasets used for testing, as well as the best performance score obtained on each dataset.