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Cited by in F6Publishing
For: Li M, Hsu W, Xie X, Cong J, Gao W. SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network.IEEE Trans Med Imaging. 2020;39:2289-2301. [PMID: 31985412 DOI: 10.1109/TMI.2020.2968472] [Cited by in Crossref: 17] [Cited by in F6Publishing: 8] [Article Influence: 8.5] [Reference Citation Analysis]
Number Citing Articles
1 Ballotin VR, Bigarella LG, Soldera J, Soldera J. Deep learning applied to the imaging diagnosis of hepatocellular carcinoma. Artif Intell Gastrointest Endosc 2021; 2(4): 127-135 [DOI: 10.37126/aige.v2.i4.127] [Reference Citation Analysis]
2 Krepl J, Casalegno F, Delattre E, Erö C, Lu H, Keller D, Rodarie D, Markram H, Schürmann F. Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas. Front Neuroinform 2021;15:691918. [PMID: 34393747 DOI: 10.3389/fninf.2021.691918] [Reference Citation Analysis]
3 de Farias EC, di Noia C, Han C, Sala E, Castelli M, Rundo L. Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features. Sci Rep 2021;11:21361. [PMID: 34725417 DOI: 10.1038/s41598-021-00898-z] [Reference Citation Analysis]
4 Moen TR, Chen B, Holmes DR 3rd, Duan X, Yu Z, Yu L, Leng S, Fletcher JG, McCollough CH. Low-dose CT image and projection dataset. Med Phys 2021;48:902-11. [PMID: 33202055 DOI: 10.1002/mp.14594] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
5 Leng L, Yang Z, Kim C, Zhang Y. A Light-Weight Practical Framework for Feces Detection and Trait Recognition. Sensors (Basel) 2020;20:E2644. [PMID: 32384651 DOI: 10.3390/s20092644] [Cited by in Crossref: 11] [Cited by in F6Publishing: 1] [Article Influence: 5.5] [Reference Citation Analysis]
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7 Kulathilake KASH, Abdullah NA, Bandara AMRR, Lai KW. InNetGAN: Inception Network-Based Generative Adversarial Network for Denoising Low-Dose Computed Tomography. J Healthc Eng 2021;2021:9975762. [PMID: 34552709 DOI: 10.1155/2021/9975762] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Fu F, Zhou Y, Zhang Y, Chen H. Lung cancer screening strategy for non-high-risk individuals: a narrative review. Transl Lung Cancer Res 2021;10:452-61. [PMID: 33569326 DOI: 10.21037/tlcr-20-943] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
9 Kulathilake KASH, Abdullah NA, Sabri AQM, Lai KW. A review on Deep Learning approaches for low-dose Computed Tomography restoration. Complex Intell Systems 2021;:1-33. [PMID: 34777967 DOI: 10.1007/s40747-021-00405-x] [Cited by in Crossref: 6] [Article Influence: 6.0] [Reference Citation Analysis]
10 Li Y, Pei X, Guo Y. 3D CNN classification model for accurate diagnosis of coronavirus disease 2019 using computed tomography images. J Med Imaging (Bellingham) 2021;8:017502. [PMID: 34322573 DOI: 10.1117/1.JMI.8.S1.017502] [Reference Citation Analysis]
11 Liu Y. A Method of CT Image Denoising Based on Residual Encoder-Decoder Network. J Healthc Eng 2021;2021:2384493. [PMID: 34603643 DOI: 10.1155/2021/2384493] [Reference Citation Analysis]
12 Lawrence T, Zhang L, Rogage K, Lim CP. Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization. Sensors (Basel) 2021;21:7936. [PMID: 34883940 DOI: 10.3390/s21237936] [Reference Citation Analysis]