Name
Abstract | ||
|---|---|---|
•MedGAN as a new end-to-end framework for medical image translation.•Combination of cGAN with non-adversarial losses and a new generator architecture.•Application on several medical tasks with no modifications to the hyperparameters.•MedGAN outperforms other approaches in qualitative and quantitative comparisons.•Perceptual evaluation was performed by five experienced radiologists. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.compmedimag.2019.101684 | Computerized Medical Imaging and Graphics |
Keywords | Field | DocType |
Generative adversarial networks,Deep neural networks,Image translation,PET attenuation correction,MR motion correction | Modalities,Image translation,Computer vision,Discriminator,Quantitative Evaluations,Artificial intelligence,Extractor,Generative grammar,Merge (version control),Medicine,Progressive refinement | Journal |
Volume | ISSN | Citations |
79 | 0895-6111 | 16 |
PageRank | References | Authors |
0.77 | 41 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karim Armanious | 1 | 18 | 2.15 |
| Chenming Jiang | 2 | 16 | 0.77 |
| Marc Fischer | 3 | 16 | 1.11 |
| Thomas Kustner | 4 | 33 | 6.58 |
| Tobias Hepp | 5 | 18 | 3.16 |
| Konstantin Nikolaou | 6 | 23 | 4.36 |
| Sergios Gatidis | 7 | 31 | 8.17 |
| Bin Yang | 8 | 201 | 49.22 |