Name
Abstract | ||
|---|---|---|
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to add... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TBME.2018.2814538 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Magnetic resonance imaging,Computed tomography,Generators,Image generation,Biomedical imaging,Task analysis | Modalities,Computer vision,Residual,Task analysis,Radiation dose,Medical imaging,Computer science,Image synthesis,Artificial intelligence,Deep learning,Adversarial system | Journal |
Volume | Issue | ISSN |
65 | 12 | 0018-9294 |
Citations | PageRank | References |
18 | 0.78 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Nie | 1 | 213 | 19.80 |
| Roger Trullo | 2 | 38 | 2.80 |
| Jun Lian | 3 | 83 | 7.32 |
| Li Wang | 4 | 1051 | 78.25 |
| Caroline Petitjean | 5 | 390 | 28.57 |
| Ruan Su | 6 | 559 | 53.00 |
| Qian Wang | 7 | 536 | 54.97 |
| Dinggang Shen | 8 | 7837 | 611.27 |