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For: Kagadis GC, Drazinos P, Gatos I, Tsantis S, Papadimitroulas P, Spiliopoulos S, Karnabatidis D, Theotokas I, Zoumpoulis P, Hazle JD. Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences. Phys Med Biol 2020;65:215027. [PMID: 32998480 DOI: 10.1088/1361-6560/abae06] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Destrempes F, Gesnik M, Chayer B, Roy-Cardinal MH, Olivié D, Giard JM, Sebastiani G, Nguyen BN, Cloutier G, Tang A. Quantitative ultrasound, elastography, and machine learning for assessment of steatosis, inflammation, and fibrosis in chronic liver disease. PLoS One 2022;17:e0262291. [PMID: 35085294 DOI: 10.1371/journal.pone.0262291] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Cheng MQ, Xian MF, Tian WS, Li MD, Hu HT, Li W, Zhang JC, Huang Y, Xie XY, Lu MD, Kuang M, Wang W, Ruan SM, Chen LD. RGB Three-Channel SWE-Based Ultrasomics Model: Improving the Efficiency in Differentiating Focal Liver Lesions. Front Oncol 2021;11:704218. [PMID: 34646763 DOI: 10.3389/fonc.2021.704218] [Reference Citation Analysis]
3 Bahrami A, Karimian A, Arabi H. Comparison of different deep learning architectures for synthetic CT generation from MR images. Phys Med 2021;90:99-107. [PMID: 34597891 DOI: 10.1016/j.ejmp.2021.09.006] [Reference Citation Analysis]
4 Anteby R, Klang E, Horesh N, Nachmany I, Shimon O, Barash Y, Kopylov U, Soffer S. Deep learning for noninvasive liver fibrosis classification: A systematic review. Liver Int 2021. [PMID: 34008300 DOI: 10.1111/liv.14966] [Reference Citation Analysis]
5 Barragán-Montero A, Javaid U, Valdés G, Nguyen D, Desbordes P, Macq B, Willems S, Vandewinckele L, Holmström M, Löfman F, Michiels S, Souris K, Sterpin E, Lee JA. Artificial intelligence and machine learning for medical imaging: A technology review. Phys Med 2021;83:242-56. [PMID: 33979715 DOI: 10.1016/j.ejmp.2021.04.016] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Papadimitroulas P, Brocki L, Christopher Chung N, Marchadour W, Vermet F, Gaubert L, Eleftheriadis V, Plachouris D, Visvikis D, Kagadis GC, Hatt M. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization. Physica Medica 2021;83:108-21. [DOI: 10.1016/j.ejmp.2021.03.009] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]