| | | 2002 |
| | 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 |
| | CHEN, GLuo, DZhong, NLi, DZheng, JLiao, HLi, ZLin, XChen, QZhang, CLU, YCHAN, YTRen, QWang, NFeng, Y | 2022 |
| | | 2022 |
| | Zhang, FHu, ZLi, GHuo, SMa, FCui, AXue, YHan, YGong, QGao, JBian, HMeng, ZWu, HLong, GTan, YZhang, YLin, XGao, XXu, ALi, Y | 2018 |
| | | 2006 |
| | Ma, YYue, YPan, MSun, JChu, JLin, XXu, WFeng, LChen, YChen, DShin, VYWang, XJin, H | 2015 |
| | | 2017 |
| | Xiao, CLing, SQiu, MDeng, ZChen, LZhu, AChen, YLiu, YLin, XLin, FWu, QShen, LYe, FLiu, XLi, YZhao, JYang, ZCowling, BJWebby, RZanin, MWong, SS | 2021 |
| | | 2021 |
| | | 2014 |
| | | 2019 |
| | Chen, EYHLin, XLam, MMLChan, KWChang, WCJoe, GWong, GHYChiu, CPYKhong, PLHoner, WGSu, WChan, CLWSo, KFTse, M | 2012 |
| | | 2022 |
| | Yang, JHuang, ZShu, HChen, YSun, XLiu, WDou, YXie, CLin, XHu, Y | 2012 |
| | | 2020 |
| | | 2010 |
| | | 2009 |
| | | 2009 |
