These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 37708896)

  • 1. QS-ADN: quasi-supervised artifact disentanglement network for low-dose CT image denoising by local similarity among unpaired data.
    Ruan Y; Yuan Q; Niu C; Li C; Yao Y; Wang G; Teng Y
    Phys Med Biol; 2023 Oct; 68(20):. PubMed ID: 37708896
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 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. Weakly supervised low-dose computed tomography denoising based on generative adversarial networks.
    Liao P; Zhang X; Wu Y; Chen H; Du W; Liu H; Yang H; Zhang Y
    Quant Imaging Med Surg; 2024 Aug; 14(8):5571-5590. PubMed ID: 39144020
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification.
    Lei Y; Zhang J; Shan H
    Phenomics; 2021 Dec; 1(6):257-268. PubMed ID: 36939784
    [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. 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]  

  • 10. Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising.
    Zhao F; Liu M; Gao Z; Jiang X; Wang R; Zhang L
    Comput Biol Med; 2023 Jul; 161():107029. PubMed ID: 37230021
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Investigation of Low-Dose CT Image Denoising Using Unpaired Deep Learning Methods.
    Li Z; Zhou S; Huang J; Yu L; Jin M
    IEEE Trans Radiat Plasma Med Sci; 2021 Mar; 5(2):224-234. PubMed ID: 33748562
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Self-supervised deep learning for joint 3D low-dose PET/CT image denoising.
    Zhao F; Li D; Luo R; Liu M; Jiang X; Hu J
    Comput Biol Med; 2023 Oct; 165():107391. PubMed ID: 37717529
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Noise2Context: Context-assisted learning 3D thin-layer for low-dose CT.
    Zhang Z; Liang X; Zhao W; Xing L
    Med Phys; 2021 Oct; 48(10):5794-5803. PubMed ID: 34287948
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. An unsupervised two-step training framework for low-dose computed tomography denoising.
    Kim W; Lee J; Choi JH
    Med Phys; 2024 Feb; 51(2):1127-1144. PubMed ID: 37432026
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Quasi-supervised learning for super-resolution PET.
    Yang G; Li C; Yao Y; Wang G; Teng Y
    Comput Med Imaging Graph; 2024 Apr; 113():102351. PubMed ID: 38335784
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.
    Hu R; Xie Y; Zhang L; Liu L; Luo H; Wu R; Luo D; Liu Z; Hu Z
    Quant Imaging Med Surg; 2024 Jan; 14(1):335-351. PubMed ID: 38223072
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Training low dose CT denoising network without high quality reference data.
    Jing J; Xia W; Hou M; Chen H; Liu Y; Zhou J; Zhang Y
    Phys Med Biol; 2022 Apr; 67(8):. PubMed ID: 35313298
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 9.