1080 related articles for article (PubMed ID: 36018732)
1. Domain-adaptive denoising network for low-dose CT via noise estimation and transfer learning.
Wang J; Tang Y; Wu Z; Tsui BMW; Chen W; Yang X; Zheng J; Li M
Med Phys; 2023 Jan; 50(1):74-88. PubMed ID: 36018732
[TBL] [Abstract][Full Text] [Related]
2. Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning.
Li M; Wang J; Chen Y; Tang Y; Wu Z; Qi Y; Jiang H; Zheng J; Tsui BMW
IEEE Trans Med Imaging; 2023 Sep; 42(9):2616-2630. PubMed ID: 37030685
[TBL] [Abstract][Full Text] [Related]
3. Learning low-dose CT degradation from unpaired data with flow-based model.
Liu X; Liang X; Deng L; Tan S; Xie Y
Med Phys; 2022 Dec; 49(12):7516-7530. PubMed ID: 35880375
[TBL] [Abstract][Full Text] [Related]
4. Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.
Li Q; Li R; Li S; Wang T; Cheng Y; Zhang S; Wu W; Zhao J; Qiang Y; Wang L
Med Phys; 2024 Feb; 51(2):1289-1312. PubMed ID: 36841936
[TBL] [Abstract][Full Text] [Related]
5. STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT.
Zhu L; Han Y; Xi X; Fu H; Tan S; Liu M; Yang S; Liu C; Li L; Yan B
Med Phys; 2023 Jul; 50(7):4443-4458. PubMed ID: 36708286
[TBL] [Abstract][Full Text] [Related]
6. Low-dose CT denoising with a high-level feature refinement and dynamic convolution network.
Yang S; Pu Q; Lei C; Zhang Q; Jeon S; Yang X
Med Phys; 2023 Jun; 50(6):3597-3611. PubMed ID: 36542402
[TBL] [Abstract][Full Text] [Related]
7. Unsupervised low-dose CT denoising using bidirectional contrastive network.
Zhang Y; Zhang R; Cao R; Xu F; Jiang F; Meng J; Ma F; Guo Y; Liu J
Comput Methods Programs Biomed; 2024 Jun; 251():108206. PubMed ID: 38723435
[TBL] [Abstract][Full Text] [Related]
8. Probabilistic self-learning framework for low-dose CT denoising.
Bai T; Wang B; Nguyen D; Jiang S
Med Phys; 2021 May; 48(5):2258-2270. PubMed ID: 33621348
[TBL] [Abstract][Full Text] [Related]
9. CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising.
Tang Y; Du Q; Wang J; Wu Z; Li Y; Li M; Yang X; Zheng J
Comput Biol Med; 2022 Aug; 147():105759. PubMed ID: 35752116
[TBL] [Abstract][Full Text] [Related]
10. Total-body low-dose CT image denoising using a prior knowledge transfer technique with a contrastive regularization mechanism.
Fu M; Duan Y; Cheng Z; Qin W; Wang Y; Liang D; Hu Z
Med Phys; 2023 May; 50(5):2971-2984. PubMed ID: 36542423
[TBL] [Abstract][Full Text] [Related]
11. A self-supervised guided knowledge distillation framework for unpaired low-dose CT image denoising.
Wang J; Tang Y; Wu Z; Du Q; Yao L; Yang X; Li M; Zheng J
Comput Med Imaging Graph; 2023 Jul; 107():102237. PubMed ID: 37116340
[TBL] [Abstract][Full Text] [Related]
12. Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information.
Tang C; Li J; Wang L; Li Z; Jiang L; Cai A; Zhang W; Liang N; Li L; Yan B
Comput Math Methods Med; 2019; 2019():8639825. PubMed ID: 31885686
[TBL] [Abstract][Full Text] [Related]
13. Adapting low-dose CT denoisers for texture preservation using zero-shot local noise-level matching.
Ko Y; Song S; Baek J; Shim H
Med Phys; 2024 Jun; 51(6):4181-4200. PubMed ID: 38478305
[TBL] [Abstract][Full Text] [Related]
14. InNetGAN: Inception Network-Based Generative Adversarial Network for Denoising Low-Dose Computed Tomography.
Kulathilake KASH; Abdullah NA; Bandara AMRR; Lai KW
J Healthc Eng; 2021; 2021():9975762. PubMed ID: 34552709
[TBL] [Abstract][Full Text] [Related]
15. Image denoising by transfer learning of generative adversarial network for dental CT.
Hegazy MAA; Cho MH; Lee SY
Biomed Phys Eng Express; 2020 Sep; 6(5):055024. PubMed ID: 33444255
[TBL] [Abstract][Full Text] [Related]
16. HCformer: Hybrid CNN-Transformer for LDCT Image Denoising.
Yuan J; Zhou F; Guo Z; Li X; Yu H
J Digit Imaging; 2023 Oct; 36(5):2290-2305. PubMed ID: 37386333
[TBL] [Abstract][Full Text] [Related]
17. X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels.
Du Q; Tang Y; Wang J; Hou X; Wu Z; Li M; Yang X; Zheng J
Comput Biol Med; 2023 Jan; 152():106419. PubMed ID: 36527781
[TBL] [Abstract][Full Text] [Related]
18. Structure-preserved meta-learning uniting network for improving low-dose CT quality.
Zhu M; Mao Z; Li D; Wang Y; Zeng D; Bian Z; Ma J
Phys Med Biol; 2022 Dec; 67(24):. PubMed ID: 36351294
[No Abstract] [Full Text] [Related]
19. Transfer learning framework for low-dose CT reconstruction based on marginal distribution adaptation in multiscale.
Yang M; Wang J; Zhang Z; Li J; Liu L
Med Phys; 2023 Mar; 50(3):1450-1465. PubMed ID: 36321246
[TBL] [Abstract][Full Text] [Related]
20. Artifact-Assisted multi-level and multi-scale feature fusion attention network for low-dose CT denoising.
Cui X; Guo Y; Zhang X; Shangguan H; Liu B; Wang A
J Xray Sci Technol; 2022; 30(5):875-889. PubMed ID: 35694948
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
