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For: Zhang F, Wang Q, Li H. Automatic Segmentation of the Gross Target Volume in Non-Small Cell Lung Cancer Using a Modified Version of ResNet. Technol Cancer Res Treat 2020;19:153303382094748. [DOI: 10.1177/1533033820947484] [Cited by in Crossref: 10] [Cited by in F6Publishing: 12] [Article Influence: 3.3] [Reference Citation Analysis]
Number Citing Articles
1 Fang L, Wang X. Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation. Biomedical Signal Processing and Control 2023;79:104027. [DOI: 10.1016/j.bspc.2022.104027] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 Xie H, Chen Z, Deng J, Zhang J, Duan H, Li Q. Automatic segmentation of the gross target volume in radiotherapy for lung cancer using transresSEUnet 2.5D Network. J Transl Med 2022;20:524. [DOI: 10.1186/s12967-022-03732-w] [Reference Citation Analysis]
3 Savjani RR, Lauria M, Bose S, Deng J, Yuan Y, Andrearczyk V. Automated Tumor Segmentation in Radiotherapy. Seminars in Radiation Oncology 2022;32:319-329. [DOI: 10.1016/j.semradonc.2022.06.002] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Fang L, Wang X. Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation.. [DOI: 10.21203/rs.3.rs-1014002/v1] [Reference Citation Analysis]
5 Shen J, Zhang F, Di M, Shen J, Wang S, Chen Q, Chen Y, Liu Z, Lian X, Ma J, Pang T, Dong T, Wang B, Guan Q, He L, Zhang Y, Liang H. Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes. Thorac Cancer 2022. [PMID: 36085253 DOI: 10.1111/1759-7714.14638] [Reference Citation Analysis]
6 Falahatpour Z, Geramifar P, Mahdavi SR, Abdollahi H, Salimi Y, Nikoofar A, Ay MR. Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy. J Appl Clin Med Phys 2022;:e13696. [PMID: 35699200 DOI: 10.1002/acm2.13696] [Reference Citation Analysis]
7 Primakov SP, Ibrahim A, van Timmeren JE, Wu G, Keek SA, Beuque M, Granzier RWY, Lavrova E, Scrivener M, Sanduleanu S, Kayan E, Halilaj I, Lenaers A, Wu J, Monshouwer R, Geets X, Gietema HA, Hendriks LEL, Morin O, Jochems A, Woodruff HC, Lambin P. Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nat Commun 2022;13:3423. [PMID: 35701415 DOI: 10.1038/s41467-022-30841-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
8 Chen W, Yang F, Zhang X, Xu X, Qiao X. MAU-Net: Multiple Attention 3D U-Net for Lung Cancer Segmentation on CT Images. Procedia Computer Science 2021;192:543-52. [DOI: 10.1016/j.procs.2021.08.056] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]