# Deep Learning in the Biomedical Applications: Recent and Future Status
## 6 Discussions and Outlooks
### Emergent Architectures: the Generative Adversarial Networks  
One of the most emerging architectures used in the biomedical applications are generative adversarial networks (GANs) (Figure 15). GANs provide a way of data augmentation to enlarge the deep representations without extensively annotated training data [281]. Proposed in 2014 by [PERSON] [282], a GAN includes two deep networks: a generator and a discriminator. The first network is seen in a common analogy: an art forger who creates forgeries with the aim of making realistic images. The discriminator represents the art expert who has to distinguish between the synthetic and the authentic images [281; 283]. The training of a GAN requires both finding the parameters of a discriminator and a generator, the discriminator having to maximize its classification accuracy and the generator having to confuse the discriminator as much as possible [281]. During the training process, only one of the two networks is concerned by the parameters updating. The second one keeps its own parameters frozen. Several variants of the GANs have recently been developed from the original fully-connected architecture proposed by [PERSON] [282] such as convolutional GANs, conditional GANs, adversarial autoencoders (AAE) and class experts GAN (see [281; 283; 284; 285; 286]). GANs are recently applied in all biomedical fields such as in the Omics for a protein modeling by [PERSON] et al. [156], who considered loop modeling as an image inpainting problem with the generative network having to capture the context of the loop region with a prediction of the missing area. In the BBMI applications, such as cardiac ECG Applications, a novel concept that embeds a generative variational autoencoder (VAE) into the objective function of Bayesian optimization was applied to estimating tissue excitability in a cardiac electrophysiological model by [PERSON] et al. [287]. In [288], a deep generative model is trained to generate the spatiotemporal dynamics of transmembrane potential (TMP). In bioimaging histology applications, to classify the newly given prostate datasets into low and high Gleason grade, an adversarial training is used to minimize the distribution discrepancy at the feature space, with the loss function adopted from the GAN [289]. In [290], a cascaded of refinement GANs for phase contrast microscopy image super-resolution is proposed. Most research using GANs concerns medical imaging applications. The main objectives are: image quality enhancement, image reconstruction, crafted images generation, image registration and segmentation. Figure 15: Distribution of published papers that use Generative Adversarial Networks (GANs) in biomedical applications. The statistics are obtained from Google Scholar. The first objective of the GANs in medical imaging is to enhance the image quality. A conditional GAN is used for the reduction of metal artifacts (RMA) in computed tomography (CT) ear images of cochlear implants (CIs) recipients in [291], and to estimate the high-quality full-dose PET images from low-dose ones in [292]. To reduce the artifacts from sparsely reconstructed cone-beam CT (CBCT) images, [PERSON] et al. [293] introduced a least squares generative adversarial networks (LSGAN), which is formulated under an image-to-image generative model. [PERSON] et al. [294] introduced a 3D neural network design, namely a multi-level densely connected super-resolution network (mDCSRN) with a GAN to generate a high-resolution (HR) magnetic resonance images (MRI) from one single low-resolution (LR) input image. Similarly, a deep residual network (ResNet) is used as a part of a GAN and trained to enhance the quality of ultrasound images [295]. In [296], a noise reducing generator based on a CNN together with a GAN is developed. In image reconstruction methods, 3D anatomical structures remain a challenge when limited number of 2D views are used to reduce cost and radiation exposure to patients.