Name
Title | ||
|---|---|---|
Generative adversarial networks and adversarial methods in biomedical image analysis. |
Abstract | ||
|---|---|---|
Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game. Adversarial techniques have been extensively used to synthesize and analyze biomedical images. We provide an introduction to GANs and adversarial methods, with an overview of biomedical image analysis tasks that have benefited from such methods. We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Computer science,Artificial intelligence,Generative grammar,Artificial neural network,Machine learning,Adversarial system |
DocType | Volume | Citations |
Journal | abs/1810.10352 | 2 |
PageRank | References | Authors |
0.37 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| jelmer m wolterink | 1 | 245 | 19.53 |
| Konstantinos Kamnitsas | 2 | 44 | 4.63 |
| Christian Ledig | 3 | 489 | 27.08 |
| Ivana Isgum | 4 | 766 | 50.08 |