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 *

143 related articles for article (PubMed ID: 38424278)

  • 1. A Hybrid Framework of Dual-Domain Signal Restoration and Multi-depth Feature Reinforcement for Low-Dose Lung CT Denoising.
    Chi J; Sun Z; Tian S; Wang H; Wang S
    J Imaging Inform Med; 2024 Aug; 37(4):1944-1959. PubMed ID: 38424278
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. A novel denoising method for CT images based on U-net and multi-attention.
    Zhang J; Niu Y; Shangguan Z; Gong W; Cheng Y
    Comput Biol Med; 2023 Jan; 152():106387. PubMed ID: 36495750
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Multi-scale feature aggregation and fusion network with self-supervised multi-level perceptual loss for textures preserving low-dose CT denoising.
    Zhang Y; Wan Z; Wang D; Meng J; Ma F; Guo Y; Liu J; Li G; Liu Y
    Phys Med Biol; 2024 Apr; 69(10):. PubMed ID: 38593821
    [No Abstract]   [Full Text] [Related]  

  • 7. Low-Dose CT Image Super-resolution Network with Noise Inhibition Based on Feedback Feature Distillation Mechanism.
    Chi J; Wei X; Sun Z; Yang Y; Yang B
    J Imaging Inform Med; 2024 Aug; 37(4):1902-1921. PubMed ID: 38378965
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Dual-domain fusion deep convolutional neural network for low-dose CT denoising.
    Li Z; Liu Y; Chen Y; Shu H; Lu J; Gui Z
    J Xray Sci Technol; 2023; 31(4):757-775. PubMed ID: 37212059
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. A novel denoising method for low-dose CT images based on transformer and CNN.
    Zhang J; Shangguan Z; Gong W; Cheng Y
    Comput Biol Med; 2023 Sep; 163():107162. PubMed ID: 37327755
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Compound feature attention network with edge enhancement for low-dose CT denoising.
    Wang S; Liu Y; Zhang P; Chen P; Li Z; Yan R; Li S; Hou R; Gui Z
    J Xray Sci Technol; 2023; 31(5):915-933. PubMed ID: 37355934
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Incorporation of residual attention modules into two neural networks for low-dose CT denoising.
    Li M; Du Q; Duan L; Yang X; Zheng J; Jiang H; Li M
    Med Phys; 2021 Jun; 48(6):2973-2990. PubMed ID: 33890681
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising.
    Bera S; Biswas PK
    IEEE Trans Med Imaging; 2021 Dec; 40(12):3663-3673. PubMed ID: 34224348
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Texture-aware dual domain mapping model for low-dose CT reconstruction.
    Wang H; Zhao X; Liu W; Li LC; Ma J; Guo L
    Med Phys; 2022 Jun; 49(6):3860-3873. PubMed ID: 35297051
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images.
    Kim B; Han M; Shim H; Baek J
    Med Phys; 2019 Sep; 46(9):3906-3923. PubMed ID: 31306488
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network.
    Li M; Hsu W; Xie X; Cong J; Gao W
    IEEE Trans Med Imaging; 2020 Jul; 39(7):2289-2301. PubMed ID: 31985412
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.