196 related articles for article (PubMed ID: 36810105)
1. Synthesizing multi-frame high-resolution fluorescein angiography images from retinal fundus images using generative adversarial networks.
Li P; He Y; Wang P; Wang J; Shi G; Chen Y
Biomed Eng Online; 2023 Feb; 22(1):16. PubMed ID: 36810105
[TBL] [Abstract][Full Text] [Related]
2. Lesion-aware generative adversarial networks for color fundus image to fundus fluorescein angiography translation.
Huang K; Li M; Yu J; Miao J; Hu Z; Yuan S; Chen Q
Comput Methods Programs Biomed; 2023 Feb; 229():107306. PubMed ID: 36580822
[TBL] [Abstract][Full Text] [Related]
3. Unsupervised arterial spin labeling image superresolution via multiscale generative adversarial network.
Cui J; Gong K; Han P; Liu H; Li Q
Med Phys; 2022 Apr; 49(4):2373-2385. PubMed ID: 35048390
[TBL] [Abstract][Full Text] [Related]
4. Perception-oriented generative adversarial network for retinal fundus image super-resolution.
Zhao L; Chi H; Zhong T; Jia Y
Comput Biol Med; 2024 Jan; 168():107708. PubMed ID: 37995535
[TBL] [Abstract][Full Text] [Related]
5. AMD-GAN: Attention encoder and multi-branch structure based generative adversarial networks for fundus disease detection from scanning laser ophthalmoscopy images.
Xie H; Lei H; Zeng X; He Y; Chen G; Elazab A; Yue G; Wang J; Zhang G; Lei B
Neural Netw; 2020 Dec; 132():477-490. PubMed ID: 33039786
[TBL] [Abstract][Full Text] [Related]
6. HRGAN: A Generative Adversarial Network Producing Higher-Resolution Images than Training Sets.
Park M; Lee M; Yu S
Sensors (Basel); 2022 Feb; 22(4):. PubMed ID: 35214337
[TBL] [Abstract][Full Text] [Related]
7. Retinal image synthesis from multiple-landmarks input with generative adversarial networks.
Yu Z; Xiang Q; Meng J; Kou C; Ren Q; Lu Y
Biomed Eng Online; 2019 May; 18(1):62. PubMed ID: 31113438
[TBL] [Abstract][Full Text] [Related]
8. A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs.
Tavakkoli A; Kamran SA; Hossain KF; Zuckerbrod SL
Sci Rep; 2020 Dec; 10(1):21580. PubMed ID: 33299065
[TBL] [Abstract][Full Text] [Related]
9. Texture transformer super-resolution for low-dose computed tomography.
Zhou S; Yu L; Jin M
Biomed Phys Eng Express; 2022 Nov; 8(6):. PubMed ID: 36301699
[TBL] [Abstract][Full Text] [Related]
10. Synthesizing high-resolution magnetic resonance imaging using parallel cycle-consistent generative adversarial networks for fast magnetic resonance imaging.
Xie H; Lei Y; Wang T; Roper J; Dhabaan AH; Bradley JD; Liu T; Mao H; Yang X
Med Phys; 2022 Jan; 49(1):357-369. PubMed ID: 34821395
[TBL] [Abstract][Full Text] [Related]
11. FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution.
Jiang M; Zhi M; Wei L; Yang X; Zhang J; Li Y; Wang P; Huang J; Yang G
Comput Med Imaging Graph; 2021 Sep; 92():101969. PubMed ID: 34411966
[TBL] [Abstract][Full Text] [Related]
12. Fundus GAN - GAN-based Fundus Image Synthesis for Training Retinal Image Classifiers.
Shenkut D; Bhagavatula V
Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():2185-2189. PubMed ID: 36086632
[TBL] [Abstract][Full Text] [Related]
13. Motion artifact removal in coronary CT angiography based on generative adversarial networks.
Zhang L; Jiang B; Chen Q; Wang L; Zhao K; Zhang Y; Vliegenthart R; Xie X
Eur Radiol; 2023 Jan; 33(1):43-53. PubMed ID: 35829786
[TBL] [Abstract][Full Text] [Related]
14. Super-Resolution Reconstruction of CT Images Based on Multi-scale Information Fused Generative Adversarial Networks.
Liu X; Su S; Gu W; Yao T; Shen J; Mo Y
Ann Biomed Eng; 2024 Jan; 52(1):57-70. PubMed ID: 38064116
[TBL] [Abstract][Full Text] [Related]
15. Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients.
Tie X; Lam SK; Zhang Y; Lee KH; Au KH; Cai J
Med Phys; 2020 Apr; 47(4):1750-1762. PubMed ID: 32012292
[TBL] [Abstract][Full Text] [Related]
16. Translation of paired fundus photographs to fluorescein angiographs with energy-based cycle-consistent adversarial networks.
Kang TS; Shon K; Park S; Lee W; Kim BJ; Han YS
Medicine (Baltimore); 2023 Jul; 102(27):e34161. PubMed ID: 37417629
[TBL] [Abstract][Full Text] [Related]
17. A novel diagnostic information based framework for super-resolution of retinal fundus images.
Das V; Dandapat S; Bora PK
Comput Med Imaging Graph; 2019 Mar; 72():22-33. PubMed ID: 30772075
[TBL] [Abstract][Full Text] [Related]
18. [A new approach for studying the retinal and choroidal circulation].
Yoneya S
Nippon Ganka Gakkai Zasshi; 2004 Dec; 108(12):836-61; discussion 862. PubMed ID: 15656089
[TBL] [Abstract][Full Text] [Related]
19. [Super-resolution construction of intravascular ultrasound images using generative adversarial networks].
Wu Y; Yang F; Huang J; Liu Y
Nan Fang Yi Ke Da Xue Xue Bao; 2019 Jan; 39(1):82-87. PubMed ID: 30692071
[TBL] [Abstract][Full Text] [Related]
20. Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks.
Zhao M; Liu X; Liu H; Wong KKL
Comput Med Imaging Graph; 2020 Mar; 80():101698. PubMed ID: 31935666
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
