Kazeminia S, Baur C, Kuijper A, Van Ginneken B, Navab N, Albarqouni S, Mukhopadhyay A (2020)
Publication Type: Journal article, Review article
Publication year: 2020
Book Volume: 109
Article Number: 101938
DOI: 10.1016/j.artmed.2020.101938
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/.
APA:
Kazeminia, S., Baur, C., Kuijper, A., Van Ginneken, B., Navab, N., Albarqouni, S., & Mukhopadhyay, A. (2020). GANs for medical image analysis. Artificial Intelligence in Medicine, 109. https://doi.org/10.1016/j.artmed.2020.101938
MLA:
Kazeminia, Salome, et al. "GANs for medical image analysis." Artificial Intelligence in Medicine 109 (2020).
BibTeX: Download