Semi-supervised Assessment of Incomplete LV Coverage in Cardiac MRI Using Generative Adversarial Nets

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Style transfer-based image synthesis as an efficient regularization technique in deep learning (2019, background): “Recently, GANs started gaining attention in the field of medical image processing to solve tasks such as de-noising [29], segmentation [30], detection [31] and classification [32].”
Generative Adversarial Network in Medical Imaging: A Review (2018, background): “Zhang et al. (2017c) proposed to use both annotated and unannotated images in the segmentation pipeline.”
Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training (2017, methodology): “GANs have also been used for segmentation [31], [32], detection [33], reconstruction [34], [35] and classification [36].”
Chest x-ray generation and data augmentation for cardiovascular abnormality classification (2018, background): “But the majority of utilization of GANs in the future will be for tasks of segmentation, detection, and classification [13-15].”
Deep learning in medical imaging and radiation therapy. (2018, background): “Zhang et al. 356 applied the same strategy on the semantic segmentation task, where the discriminator not only evaluated the segmentation results itself but also tried to differentiate the labeled and unlabeled data.”