150 related articles for article (PubMed ID: 36279348)
1. Transformer With Double Enhancement for Low-Dose CT Denoising.
Li H; Yang X; Yang S; Wang D; Jeon G
IEEE J Biomed Health Inform; 2023 Oct; 27(10):4660-4671. PubMed ID: 36279348
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
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. 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]
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. 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]
8. Two stage residual CNN for texture denoising and structure enhancement on low dose CT image.
Huang L; Jiang H; Li S; Bai Z; Zhang J
Comput Methods Programs Biomed; 2020 Feb; 184():105115. PubMed ID: 31627148
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising.
Wang H; Chi J; Wu C; Yu X; Wu H
J Digit Imaging; 2023 Aug; 36(4):1894-1909. PubMed ID: 37118101
[TBL] [Abstract][Full Text] [Related]
11. A Review of deep learning methods for denoising of medical low-dose CT images.
Zhang J; Gong W; Ye L; Wang F; Shangguan Z; Cheng Y
Comput Biol Med; 2024 Mar; 171():108112. PubMed ID: 38387380
[TBL] [Abstract][Full Text] [Related]
12. Self-adaption and texture generation: A hybrid loss function for low-dose CT denoising.
Wang Z; Liu M; Cheng X; Zhu J; Wang X; Gong H; Liu M; Xu L
J Appl Clin Med Phys; 2023 Sep; 24(9):e14113. PubMed ID: 37571834
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. Low-Dose CT Image Denoising Based on Improved DD-Net and Local Filtered Mechanism.
Liu H; Jin X; Liu L; Jin X
Comput Intell Neurosci; 2022; 2022():2692301. PubMed ID: 35965772
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. A densely connected LDCT image denoising network based on dual-edge extraction and multi-scale attention under compound loss.
Lina J; Xu H; Aimin H; Beibei J; Zhiguo G
J Xray Sci Technol; 2023; 31(6):1207-1226. PubMed ID: 37742690
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
