Name
Abstract | ||
|---|---|---|
The development of medical imaging techniques has greatly supported clinical decision making. However, poor imaging quality, such as non-uniform illumination or imbalanced intensity, brings challenges for automated screening, analysis and diagnosis of diseases. Previously, bi-directional GANs (e.g., CycleGAN), have been proposed to improve the quality of input images without the requirement of pai... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TMI.2021.3101937 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Lighting,Medical diagnostic imaging,Image enhancement,Task analysis,Bidirectional control,Training,Generative adversarial networks | Journal | 40 |
Issue | ISSN | Citations |
12 | 0278-0062 | 3 |
PageRank | References | Authors |
0.38 | 21 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuhui Ma | 1 | 20 | 2.08 |
| Jiang Liu | 2 | 335 | 34.30 |
| Yonghuai Liu | 3 | 675 | 61.65 |
| Huazhu Fu | 4 | 1235 | 65.07 |
| Yan Hu | 5 | 18 | 9.84 |
| Jun Cheng | 6 | 214 | 20.65 |
| Hong Qi | 7 | 3 | 0.72 |
| Yufei Wu | 8 | 3 | 0.38 |
| Jiong Zhang | 9 | 317 | 29.35 |
| Yitian Zhao | 10 | 246 | 33.15 |