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Cited by in F6Publishing

For:	Dai M, Liu Y, Hu Y, Li G, Zhang J, Xiao Z, Lv F. Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas. Eur Radiol 2022;32:7988-97. [PMID: 35583712 DOI: 10.1007/s00330-022-08783-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]

For:

Dai M, Liu Y, Hu Y, Li G, Zhang J, Xiao Z, Lv F. Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas. Eur Radiol 2022;32:7988-97. [PMID: 35583712 DOI: 10.1007/s00330-022-08783-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]

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1	Wang Z, Zhao T, Zhang H, Zhang C, Duan T, Li X, Xu L. Deep transfer learning radiomics based on two-dimensional ultrasound for predicting the efficacy of neoadjuvant chemotherapy in breast cancer.. [DOI: 10.21203/rs.3.rs-2427398/v1] [Reference Citation Analysis]
2	Chen W, Gong M, Zhou D, Zhang L, Kong J, Jiang F, Feng S, Yuan R. CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer. Front Oncol 2022;12:1019749. [PMID: 36544709 DOI: 10.3389/fonc.2022.1019749] [Reference Citation Analysis]
3	Zheng Y, Che J, Yuan M, Wu Z, Pang J, Zhou R, Li X, Dong C. A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma. Academic Radiology 2022. [DOI: 10.1016/j.acra.2022.11.007] [Reference Citation Analysis]