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• W3047625747 endingPage "101938" @default.
• W3047625747 startingPage "101938" @default.
• W3047625747 abstract "Generative adversarial networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In this review paper, a broad overview of recent literature on GANs for medical applications is given, the shortcomings and opportunities of the proposed methods are thoroughly discussed, and potential future work is elaborated. We review the most relevant papers published until the submission date. For quick access, essential details such as the underlying method, datasets, and performance are tabulated. An interactive visualization that categorizes all papers to keep the review alive is available at http://livingreview.in.tum.de/GANs_for_Medical_Applications/." @default.
• W3047625747 created "2020-08-13" @default.
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• W3047625747 date "2020-09-01" @default.
• W3047625747 modified "2023-10-17" @default.
• W3047625747 title "GANs for medical image analysis" @default.
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• W3047625747 doi "https://doi.org/10.1016/j.artmed.2020.101938" @default.
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