Abstract
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.
Keywords
Adversarial systemComputer scienceDiscriminatorArtificial intelligenceGenerative grammarConsistency (knowledge bases)Machine learningImage translationSegmentationDomain (mathematical analysis)Image (mathematics)Translation (biology)Adaptation (eye)Medical imagingDeep learningMathematics
MeSH Terms
AlgorithmsHumansImage ProcessingComputer-AssistedMagnetic Resonance ImagingNeural NetworksComputer
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Publication Info
- Year
- 2019
- Type
- review
- Volume
- 58
- Pages
- 101552-101552
- Citations
- 1692
- Access
- Closed
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Cite This
Yi Xin,
Ekta Walia,
Paul Babyn
(2019).
Generative adversarial network in medical imaging: A review.
Medical Image Analysis
, 58
, 101552-101552.
https://doi.org/10.1016/j.media.2019.101552
Identifiers
- DOI
- 10.1016/j.media.2019.101552
- PMID
- 31521965
- arXiv
- 1809.07294