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Cited by in F6Publishing
For: Zollner FG, Kocinski M, Hansen L, Golla A, Trbalic AS, Lundervold A, Materka A, Rogelj P. Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects. IEEE Access 2021;9:71577-605. [DOI: 10.1109/access.2021.3078430] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Raj A, Tollens F, Hansen L, Golla A, Schad LR, Nörenberg D, Zöllner FG. Deep Learning-Based Total Kidney Volume Segmentation in Autosomal Dominant Polycystic Kidney Disease Using Attention, Cosine Loss, and Sharpness Aware Minimization. Diagnostics 2022;12:1159. [DOI: 10.3390/diagnostics12051159] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
2 Sun P, Mo Z, Hu F, Liu F, Mo T, Zhang Y, Chen Z. Kidney Tumor Segmentation Based on FR2PAttU-Net Model. Front Oncol 2022;12:853281. [DOI: 10.3389/fonc.2022.853281] [Reference Citation Analysis]
3 Zhang C, Schwartz M, Küstner T, Martirosian P, Seith F. Multiparametric Functional MRI of the Kidney: Current State and Future Trends with Deep Learning Approaches. Rofo 2022. [PMID: 35272360 DOI: 10.1055/a-1775-8633] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Klepaczko A, Majos M, Stefańczyk L, Ejkefjord E, Lundervold A. Whole kidney and renal cortex segmentation in contrast-enhanced MRI using a joint classification and segmentation convolutional neural network. Biocybernetics and Biomedical Engineering 2022. [DOI: 10.1016/j.bbe.2022.02.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]