Name
Title | ||
|---|---|---|
Mutually Improved Endoscopic Image Synthesis and Landmark Detection in Unpaired Image-to-Image Translation |
Abstract | ||
|---|---|---|
The CycleGAN framework allows for unsupervised image-to-image translation of unpaired data. In a scenario of surgical training on a physical surgical simulator, this method can be used to transform endoscopic images of phantoms into images which more closely resemble the intra-operative appearance of the same surgical target structure. This can be viewed as a novel augmented reality approach, whic... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JBHI.2021.3099858 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Task analysis,Surgery,Valves,Maintenance engineering,Training,Semantics,Generative adversarial networks | Journal | 26 |
Issue | ISSN | Citations |
1 | 2168-2194 | 1 |
PageRank | References | Authors |
0.38 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lalith Sharan | 1 | 3 | 2.13 |
| Gabriele Romano | 2 | 1 | 0.72 |
| Sven Koehler | 3 | 1 | 0.38 |
| Halvar Kelm | 4 | 1 | 0.38 |
| Matthias Karck | 5 | 1 | 0.38 |
| Raffaele De Simone | 6 | 1 | 0.38 |
| Sandy Engelhardt | 7 | 1 | 0.38 |