Image Super Resolution Using Generative Adversarial Networks and Local Saliency Maps for Retinal Image Analysis

By D. Mahapatra, Behzad Bozorgtabar, Sajini Hewavitharanage, Rahil Garnavi. 2017

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Visual Feature Attribution Using Wasserstein GANs (2017, methodology): “In the context of medical images, GANs have been applied to super-resolution in retinal fundus images [34], for semi-supervised cardiac segmentation [64], synthesising computed tomography images from MR images[40, 60] and intraoperative motion modelling [23].”
GANs for Medical Image Analysis (2018, background): “In [92] and [93] pixel-wise losses, in [6] and [94] perceptual losses, in [95] a saliency loss, and in [96] Voronoi-vectorization and regularization losses are proposed to be added to the adversarial loss.”
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks (2018, methodology): “GANs have been used to generate a motion model from a single preoperative MRI [10], upsample a lowresolution fundus image [13], create a synthetic head CT from a brain MRI [16], and synthesizing T2-weight MRI from T1-weighted ones (and vice-versa) [5].”
GAN-based synthetic brain MR image generation (2018, background): “Therefore, many medical imaging researchers have begun to use GANs recently, such as in image super-resolution [16], anomaly detection [17], and estimating CT images from the corresponding MR images [18].”