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Cited by in F6Publishing
For: Lin Y, Lin C, Lu H, Chiang H, Wang H, Huang Y, Ng S, Hong J, Yen T, Lai C, Lin G. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer. Eur Radiol 2020;30:1297-305. [DOI: 10.1007/s00330-019-06467-3] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Jia D, Zhou J, Zhang C. Detection of cervical cells based on improved SSD network. Multimed Tools Appl. [DOI: 10.1007/s11042-021-11015-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
2 Kido A, Nakamoto Y. Implications of the new FIGO staging and the role of imaging in cervical cancer. Br J Radiol 2021;94:20201342. [PMID: 33989030 DOI: 10.1259/bjr.20201342] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Jin J, Zhu H, Zhang J, Ai Y, Zhang J, Teng Y, Xie C, Jin X. Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer. Front Oncol 2020;10:614201. [PMID: 33680934 DOI: 10.3389/fonc.2020.614201] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
4 Lin C, Wu J, Li C, Chen P, Pai N, Kuo Y. Enhancement of Chest X-Ray Images to Improve Screening Accuracy Rate Using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier. IEEE Photonics J 2020;12:1-18. [DOI: 10.1109/jphot.2020.3013193] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
5 Han C, Ma S, Liu X, Liu Y, Li C, Zhang Y, Zhang X, Wang X. Radiomics Models Based on Apparent Diffusion Coefficient Maps for the Prediction of High‐Grade Prostate Cancer at Radical Prostatectomy: Comparison With Preoperative Biopsy. J Magn Reson Imaging. [DOI: 10.1002/jmri.27565] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Gou S, Xu Y, Yang H, Tong N, Zhang X, Wei L, Zhao L, Zheng M, Liu W. Automated cervical tumor segmentation on MR images using multi-view feature attention network. Biomedical Signal Processing and Control 2022;77:103832. [DOI: 10.1016/j.bspc.2022.103832] [Reference Citation Analysis]
7 Hou X, Shen G, Zhou L, Li Y, Wang T, Ma X. Artificial Intelligence in Cervical Cancer Screening and Diagnosis. Front Oncol 2022;12:851367. [DOI: 10.3389/fonc.2022.851367] [Reference Citation Analysis]
8 Song J, Hu Q, Ma Z, Zhao M, Chen T, Shi H. Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients. Eur Radiol 2021;31:6938-48. [PMID: 33585992 DOI: 10.1007/s00330-021-07735-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
9 Chen Y, Xing L, Yu L, Bagshaw HP, Buyyounouski MK, Han B. Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch UNet. Med Phys 2020;47:6421-9. [PMID: 33012016 DOI: 10.1002/mp.14517] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
10 Tong H, Sun J, Fang J, Zhang M, Liu H, Xia R, Zhou W, Liu K, Chen X. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study. Front Immunol 2022;13:859323. [DOI: 10.3389/fimmu.2022.859323] [Reference Citation Analysis]
11 Duan J, Qiu Q, Zhu J, Shang D, Dou X, Sun T, Yin Y, Meng X. Reproducibility for Hepatocellular Carcinoma CT Radiomic Features: Influence of Delineation Variability Based on 3D-CT, 4D-CT and Multiple-Parameter MR Images. Front Oncol 2022;12:881931. [DOI: 10.3389/fonc.2022.881931] [Reference Citation Analysis]
12 Kalantar R, Lin G, Winfield JM, Messiou C, Lalondrelle S, Blackledge MD, Koh DM. Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges. Diagnostics (Basel) 2021;11:1964. [PMID: 34829310 DOI: 10.3390/diagnostics11111964] [Reference Citation Analysis]
13 Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021;11:4431-60. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Zhao J, Zhang W, Fan CL, Zhang J, Yuan F, Liu SY, Li FY, Song B. Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study. Eur J Radiol 2021;138:109631. [PMID: 33711571 DOI: 10.1016/j.ejrad.2021.109631] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Reference Citation Analysis]
16 Manganaro L, Nicolino GM, Dolciami M, Martorana F, Stathis A, Colombo I, Rizzo S. Radiomics in cervical and endometrial cancer. Br J Radiol 2021;94:20201314. [PMID: 34233456 DOI: 10.1259/bjr.20201314] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Nagy M, Radakovich N, Nazha A. Machine Learning in Oncology: What Should Clinicians Know? JCO Clin Cancer Inform 2020;4:799-810. [PMID: 32926637 DOI: 10.1200/CCI.20.00049] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
18 Kurata Y, Nishio M, Moribata Y, Kido A, Himoto Y, Otani S, Fujimoto K, Yakami M, Minamiguchi S, Mandai M, Nakamoto Y. Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network. Sci Rep 2021;11:14440. [PMID: 34262088 DOI: 10.1038/s41598-021-93792-7] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
19 Gassenmaier S, Afat S, Nickel D, Mostapha M, Herrmann J, Othman AE. Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality. Eur J Radiol 2021;137:109600. [PMID: 33610853 DOI: 10.1016/j.ejrad.2021.109600] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]