Image-to-image translation has gained popularity in the medical field to
transform images from one domain to another. Medical image synthesis via domain
transformation is advantageous in its ability to augment an image dataset where
images for a given class is limited. From the learning perspective, this
process contributes to data-oriented robustness of the model by inherently
broadening the model's exposure to more diverse visual data and enabling it to
learn more generalized features. In the case of generating additional
neuroimages, it is advantageous to obtain unidentifiable medical data and
augment smaller annotated datasets. This study proposes the development of a
CycleGAN model for translating neuroimages from one field strength to another
(e.g., 3 Tesla to 1.5). This model was compared to a model based on DCGAN
architecture. CycleGAN was able to generate the synthetic and reconstructed
images with reasonable accuracy. The mapping function from the source (3 Tesla)
to target domain (1.5 Tesla) performed optimally with an average PSNR value of
25.69 $\pm$ 2.49 dB and an MAE value of 2106.27 +/- 1218.37.

该研究提出了使用 CycleGAN 模型将神经图像从一种场强转化为另一种的方法，并将其与基于 DCGAN 结构的模型进行了比较，结果表明 CycleGAN 能够以较高的准确度生成合成和重构图像。

MRI 图像翻译的 CycleGAN 模型

CycleGAN Models for MRI Image Translation

While generative adversarial networks (GAN) have been widely adopted in
various topics, in this paper we generalize the standard GAN to a new
perspective by treating realness as a random variable that can be estimated
from multiple angles. In this generalized framework, referred to as
RealnessGAN, the discriminator outputs a distribution as the measure of
realness. While RealnessGAN shares similar theoretical guarantees with the
standard GAN, it provides more insights on adversarial learning. Compared to
multiple baselines, RealnessGAN provides stronger guidance for the generator,
achieving improvements on both synthetic and real-world datasets. Moreover, it
enables the basic DCGAN architecture to generate realistic images at 1024*1024
resolution when trained from scratch.

通过将真实性作为随机变量来估计，我们提出了一种新型 GAN（RealnessGAN），并将其与多个基线对比，在合成和真实数据集上产生实质性的生成器引导改善和提供关于对抗学习的深入见解。

真实还是虚构，这是问题

Real or Not Real, that is the Question

The challenging task of image outpainting (extrapolation) has received
comparatively little attention in relation to its cousin, image inpainting
(completion). Accordingly, we present a deep learning approach based on Iizuka
et al. for adversarially training a network to hallucinate past image
boundaries. We use a three-phase training schedule to stably train a DCGAN
architecture on a subset of the Places365 dataset. In line with Iizuka et al.,
we also use local discriminators to enhance the quality of our output. Once
trained, our model is able to outpaint $128 \times 128$ color images relatively
realistically, thus allowing for recursive outpainting. Our results show that
deep learning approaches to image outpainting are both feasible and promising.

通过深度学习的方式，利用基于 DCGAN 架构的图像分割技术及本地鉴别器提升图像边界的质量，从而实现图片的画外部分（extrapolation），其结果表明利用深度学习方法进行画外部分的研究既可行又具有前景。