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Cited by in F6Publishing
For: [DOI: 10.1117/12.2216509] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 0.6] [Reference Citation Analysis]
Number Citing Articles
1 Uplaonkar DS, Virupakshappa, Patil N. Modified Otsu thresholding based level set and local directional ternary pattern technique for liver tumor segmentation. Int J Syst Assur Eng Manag. [DOI: 10.1007/s13198-022-01637-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
2 Jiang H, Diao Z, Yao Y. Deep learning techniques for tumor segmentation: a review. J Supercomput 2022;78:1807-51. [DOI: 10.1007/s11227-021-03901-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
3 Ryu H, Shin SY, Lee JY, Lee KM, Kang HJ, Yi J. Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning. Eur Radiol 2021. [PMID: 33881566 DOI: 10.1007/s00330-021-07850-9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
4 Mathieu B, Crouzil A, Puel J. Interactive segmentation: a scalable superpixel-based method. J Electron Imaging 2017;26:1. [DOI: 10.1117/1.jei.26.6.061606] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]