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| | Luo, JYCheng, CKHe, LPu, YJZhang, YLin, XXu, AMLau, CWTian, XYMa, RCWJo, HHuang, Y
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| | 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 |
| | Ma, RCWHu, CTam, CHZhang, RKwan, PLeung, TFThomas, GNGo, MJHara, KSim, XHo, JSKWang, CLi, HLu, LWang, YLi, JWWang, YLam, VKLWang, JYu, WKim, YJNg, DPFujita, JPanoutsopoulou, KDay-Williams, AGLee, HMNg, ACWFang, YJKong, APSJiang, FMa, XHou, XTang, SLu, JYamauchi, TTsui, SKWWoo, JLeung, PCZhang, XTang, NLSSy, HYLiu, JWong, TYLee, JYMaeda, SXu, GCherny, SSChan, TFNg, MCYXiang, KMorris, APDIAGRAM Consortium,Keildson, SThe MuTHER Consortium,Hu, RLi, JLin, XCho, YSKadowaki, TTai, ESZeggini, EMcCarthy, MIHon, KLBaum, LTomlinson, BSo, WYBao, YChan, JCNJia, W | 2013 |