# Deep Learning in the Biomedical Applications: Recent and Future Status
## 6 Discussions and Outlooks
### Emergent Architectures: the Generative Adversarial Networks
A new deep conditional generative network for the 3D reconstruction of the fetal skull from 2D ultrasound (US) standard planes of the head routinely acquired during the fetal screening process was introduced by [PERSON] et al. [297]. This 3D reconstruction is based on the generative properties of a conditional variational autoencoders (CVAE), a deep conditional generative network for the 3D reconstruction of the fetal skull from freehand non-aligned 2D scans of the head. In another part, compressed sensing-based magnetic resonance imaging (CS-MRI) is a promising paradigm allowing to accelerate MRI acquisition by reconstructing images from only a fraction of the normally required measurements [298]. To solve this problem, deep generative adversarial networks were recently used by [PERSON] et al. [298], [PERSON] et al. [299], and [PERSON] et al. [300]. Due to the lack of annotated data, GANs are used to generate crafted images, for example to synthesize multiple realistic-looking retinal images from an unseen tubular structured annotation that contains the binary vessel morphology in [301] or to generate synthetic liver lesion images in [302]. In [303], adversarial examples are explored in medical imaging and leveraged in a constructive fashion to benchmark model performance not only on clean and noisy but also on adversarially crafted data. In [304], a conditional GAN is explored to augment artificially generated lung nodules to improve the robustness of the progressive holistically nested network (P-HNN) model for pathological lung segmentation of CT scans. In [305], a novel generative invertible networks (GIN), which is a combination of a convolutional neural network and generative adversarial networks, is proposed to extract the features from real patients and generate virtual patients, which are both visually and pathophysiologically plausible, using the features. In [306], a deep generative multi-task is developed to solve the problem of limited training data and data with lesion annotations because making the annotations is a very expensive and time consuming task. The deep generative multi-task is built upon the combination of a convolutional neural networks and a generative adversarial network. Transfer learning is also adapted to a model trained with one type of data to another. In [307], a conditional generative adversarial network is used to generate realistic chest X-ray images with different disease characteristics by conditioning its generation on a real image sample. This approach has the advantage of overcoming limitations of small training datasets by generating truly informative samples. To solve the problem of missing data in multi-modal studies, [PERSON] et al. [308] proposed a two-stage deep learning framework for Alzheimer's disease diagnosis using incomplete multi-modal imaging data. In the first stage, a 3D cycle consistent generative adversarial networks model is used for imputing the missing PET data based on their corresponding MRI data. A landmark-based multi-modal multi-instance neural network for brain disease classification is used in the second stage. Image registration can be defined as the process of aligning two or more images to find the optimal transformation that best aligns the structures of interest in the input images. [PERSON] et al. [309] introduced an adversarial similarity network, which is inspired by generative adversarial network, to automatically learn the similarity metric for training a deformable registration network. In [301], an adversarial learning approach is used to constrain convolutional neural network training for image registration. The end-to-end trained network enables efficient and fully-automated registration that only requires a magnetic resonance and transrectal ultrasound image pair as input, without anatomical labels or simulated data during inference. Generative adversarial networks are also used in medical imaging segmentation.