| | | 2019 |
| | | 2018 |
| | | 2018 |
| | | 2019 |
| | | 2021 |
| | This paper investigates the problem of pseudo-healthy synthesis that is defined as synthesizing a subject-specific pathology-free image from a pathological one. Recent approaches based on Generative Adversarial Network (GAN) have been developed for this task. However, these methods will inevitably fall into the trade-off between preserving the subject-specific identity and generating healthy-like appearances. To overcome this challenge, we propose a novel adversarial training regime, Generator v, YLi, CLin, XSun, LZhuang, YHuang, YDing, XLiu, XYu, Y | 2021 |
| | | 2021 |
| | | 2016 |
| | | 2017 |
| | | 2021 |
| | | 2014 |
| | | 2019 |
| | | 2014 |
| | | 2019 |
| | | 2019 |
