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Table 1 The characteristics of typical GAN variant techniques and examples of general tasks in general medicine and ophthalmology fields

From: Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey

GAN Techniques

Dataset

Characteristics

Task examples in general medicine

Task examples in ophthalmology

Deep convolutional GAN (DCGAN)

Images from one domain

Improved image quality using deep convolutional layers

Augmentation of CT images [88]

Fundus photographs synthesis [39]

Wasserstein GAN (WGAN)

Images from one domain

Using Wasserstein distance as a loss function

Augmentation of CT images [89]

Removing artifacts in CT [90]

Anomaly detection [25]

OCT segmentation [91]

Progressively growing GAN (PGGAN)

Images from one domain (generally high resolution)

High resolution & realistic image generation

X-ray image synthesis [92]

Data augmentation for cytological images [93]

Data augmentation for fundus photography, retinal OCT, and ocular images [40, 46, 82]

Super-resolution of fundus photographs [55]

StyleGAN

Images of one domain or multiple domains (unpaired images)

Disentanglement of representations (mapping features to low dimensions)

Augmentation of CT and MRI images in specific conditions [13, 94]

Skin image synthesis [95]

None

Conditional GAN (vector input models)

Images annotated by conditional variables

Image synthesis conditioned to specific variables

Super-resolution guided by a conditional variable [96]

Data augmentation for retinal OCT [44]

Post-intervention (orbital decompression) prediction [15]

Conditional GAN (Pix2pix and other image input models)

Paired images of two domains or classes. (Training samples should be aligned)

Supervised learning for image-to-image translation

Super-resolution for fluorescence microscopy images [97]

Domain transfer (CT → PET) [98]

Segmentation of lungs from chest X-ray [99]

CT image synthesis [100]

Domain transfer (fundus photography → angiography) [62]

Retinal OCT segmentation [36]

Retinal vessel segmentation [28]

Data augmentation for fundus photography and corneal topography [17, 47]

Super-resolution GAN (SRGAN)

Low- and high-resolution image pairs

Adopting perceptual loss to generate super-resolved realistic images

Super-resolution for dental X-ray [101]

Super-resolution for optic disc photography [56]

Cycle-consistent GAN (CycleGAN)

Unpaired images of two domains or classes

Adopting a cycle consistency for domain transfer without any paired dataset

Manipulating breast imaging [102]

Data augmentation for CT and throat images [103, 104]

Segmentation for cardiac ultrasound [105]

Denoising for fundus photography and OCT [22, 53]

Domain transfer (Ultra-widefield retinal images → classic fundus photography) [63]

StarGAN

Unpaired images of multiple domains or classes

A single network to achieve translation of multiple domains

Domain transfer between MRI contrasts [24]

None

  1. CT = computed tomography; GAN = generative adversarial network; MRI = magnetic resonance imaging; OCT = optical coherence tomography; PET = positron emission tomography