## 3 High-Dimensional Medical Imaging Analysis Based on Deep Learning
### Semi-Supervised Deep Learning Strategies for MRI
#### 3.2.2 Generative Adversarial Networks for MRI  
Generative adversarial networks (GANs) are a special type of neural network system where two networks are trained simultaneously, with one tuned on image generation and the other on discrimination. Adversarial training schemes have gained popularity in both academia and industry, due to their usefulness in counteracting domain shift and effectiveness in generating new image samples. GANs have achieved a state-of-the-art performance in many image generation tasks, such as text-to-image synthesis [94], super-resolution [95] as well as image-to-image translation [96]. Unlike deep learning, which has its roots traced back to the 1980s [96], the concept of adversarial training is relatively new but there has been significant progress recently [97]. In the medical imaging field, GANs are widely used nowadays for medical image synthesis. There are two reasons for this, as discussed below:  
(i) Although medical data sets become more accessible through public databases, most are restricted to specific medical conditions and are specific to measurement equipment and measurement protocols. Thus, availability of data for machine learning purposes still remains challenging, nevertheless synthetic data augmentation through the use of generative adversarial networks (GAN) may overcome this hurdle. Synthetic data also helps overcome privacy issues associated with medical data. It also addresses the problem of an insufficient number of positive cases that may be associated with each pathology, which limits the number of samples for training a classifier. (ii) In addition, it is also widely accepted that there is limited expertise in annotating certain types of medical images or the annotation is so laborious that only a limited number of samples are incorporated in certain databases. For certain types of medical images, scaling, rotation, flipping, translation and elastic deformation have been used as a means to systematically augment datasets to increase the number of training samples [98]. However, these transformations are rather limiting in that they do not account for additional variations resulting from different imaging protocols or sequences, or variations in tumour morphology, location and texture according to a specific pathology. GANs provide a more generic solution for augmenting training images with promising results [99]. A current problem in developing a widely accepted database to compare the performance of different learning algorithms has led to novel approaches for generating artificially training datasets. Certain types of medical images are also too expensive to generate, so there is an interest in synthesising image samples. [PERSON] et al. [100] discussed the use of an adversarial learning-based approach to synthesise medical images for medical image tissue recognition. The performance of medical image recognition models highly depends on the degree of representativeness and the ability of all the required features in the training samples. Generative adversarial networks (GANs), which consist of a generative network and a discriminative network, can be used to develop a medical image synthesis model. More specifically, deep convolutional GANs (DCGANs), Wasserstein GANs (WGANs) and boundary equilibrium GANs (BEGANs) can be used to synthesise medical images and their success rate can be compared. The justification for convolutional neural networks (CNNs) being applied in the GAN models, is that these can capture feature representations that describe a high level of semantic information in images. Synthetic images can be subsequently generated by employing the generative network mapping established. The effectiveness of the generative network can be validated by a discriminative network, which is trained to differentiate the synthetic images from real images. Through the adoption of a minimax two-player game learning routine, the generative and discriminative networks can train each other.