| | | 1-Feb-2023 |
| | | 2013 |
| | | 2009 |
| | | 2008 |
| | WANG, YLYU, ZQin, YWANG, XSUN, LZHANG, YGONG, LWU, SHan, STANG, YJia, YKwong, DLWKam, NWGuan, X | 2020 |
| | | 2019 |
| | | 2012 |
| | Zhang, JZhang, LZhang, YYang, JGuo, MSun, LPan, HFHirankarn, NYing, DZeng, SLee, TLLau, WCSChan, DTMLeung, AMHMok, CCWong, SNLee, KWHo, MHKLee, PPWChung, BHYChong, CCWong, RWSMok, TMYWong, WHSTong, KLTse, NKCLi, XPAvihingsanon, YRianthavorn, PDeekajorndej, TSuphapeetiporn, KShotelersuk, VYing, SKYFung, SKSLai, WMGarcia-Barceló, MMCherny, SSSham, PCCui, YYang, SYe, DQZhang, XJLau, YLYang, W | 2015 |
| | | 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 |
| | Zhang, YYang, JZhang, JSun, LHirankarn, NPan, HFLau, WCSChan, DTMLee, TLLeung, AMHMok, CCZhang, LWang, YShen, JJWong, SNLee, KWHo, MHKLee, PPWChung, BHYChong, CYWong, RWSMok, TMYWong, WHSTong, KLTse, NKCLi, XPAvihingsanon, YRianthavorn, PDeekajorndej, TSuphapeetiporn, KShotelersuk, VYing, SKYFung, SKSLai, WMWong, CMNg, IOLGarcia-Barcelo, MMCherny, SSCui, YSham, PCYang, SYe, DQZhang, XJLau, YLYang, W | 2015 |
| | | 2008 |
| | | 2010 |
| | | 2012 |
| | | 2022 |
| | | 2015 |
| | | 2013 |
| | | 2022 |
| | | 2018 |
| | Yang, MDeng, JLiu, YKo, KHWang, XJiao, ZWang, SHua, ZSun, LSrivastava, GLau, WCSCao, XLu, L | 2012 |
