# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Adversarial Methods
In comparison, the supervised MK-C model was also tested with the same percentages of labeled data and showed an increase in 7% in accuracy and 5% in AUC. These results show that the MK-SSAC model has relative robustness to variation in the number of labeled data. Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment (SS-DCGAN) [51]: SS-DCGAN is a model created to synthesize retinal images and determine the presence or absence of glaucoma. This model is based on the DCGAN architecture with a modification in the last output layer of D: one neuron for synthesis and three neurons for training the glaucoma classifier. D thus becomes a classifier that has to label each sample as either Normal, Glaucoma, or Synthetic. The loss function of the method was as follows:  
\[L=L_{\text{supervised}}\ +L_{\text{unsupervised}} \tag{19}\]  
where \(L_{\text{supervised}}\) for \(K\) classes is the cross-entropy loss function:  
\[L_{\text{supervised}}\ =-\mathbb{E}_{x,y\sim p_{\text{data}}\ (x,y)}log(p_{\text{model}}\ (y|x,y<K+1)) \tag{20}\]  
and \(L_{\text{unsupervised}}\) is GAN's two-player minmax game:  
\[L_{\text{unsupervised}}\ =-\Big{\{}\mathbb{E}_{x\sim p_{\text{data}}\ (x)} \ logD(x) \tag{21}\] \[+\mathbb{E}_{z\sim p_{z}(z)}log(1-D(G(z)))\Big{\}}\]  
where \(D(x)\) is the probability of \(x\) being from the real data and \(G(z)\) is the probability of \(z\) being from the generator. The dataset used for experimentation was a combination of fourteen public databases with a total of 86,926 images. Samples with glaucoma or normal labels were divided into a 70% training set (669 glaucomas and 981 normal) and a 30% testing set (287 glaucomas and 420 normal). The labeled training set, as well as all unlabeled images (84,569 samples), were used to train the model. The metrics calculated were AUC, F1-score, specificity, and sensitivity. The final results were compared to three previous models: ResNet50 [59] as well as the CNNs proposed by [PERSON] et al. [60] and [PERSON] et al. [61]. SS-DCGAN achieved the highest AUC (0.90) and F1-score (0.84), as well as the second-highest specificity and sensitivity scores among the models. Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks [62]: multi-modality imaging involves the simultaneous usage of two or more imaging modalities for one examination [63]. For example, single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), and positron emission tomography (PET) can be used to combine optical, magnetic, and radioactive reporters to detect abnormalities in the epileptic brain. Common examples of bi-modality images are PET-SPECT and PET-CT [63]. The model proposed by [PERSON] et al. was intended to synthesize high-quality bi-modality medical images using GANs by creating two sequential generative networks, each corresponding to one modality. The first modality in the sequence is the one with the least complexity, measured automatically by a complexity measurer algorithm. The goal of this is to decrease the difficulty of generating the second, more complex modality by conditioning it on the first. The proposed generator network is trained via semi-supervised learning in order to generate realistic images with a large diversity. The supervised learning model learns the joint distribution of the different modalities, while the unsupervised learning model learns the marginal distribution of the different modalities via adversarial learning. The architecture of the generator is as follows: first, a real image of modality 1 is encoded into a low-dimensional latent vector, which is then mapped to a fake image of the same modality by a decoder. For the second image, an image-to-image translator is used to create a fake image of modality 2 with the help of information gained from the previously generated fake image of modality 1.