FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation.

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Bibliographic Details
Title: FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation.
Authors: Bai J, Tang Y, Zhou Z, Islam M, Tabassum M, Almar-Munoz E, Liu H, Meng H, Lv N, Deng B, Chen Y, Peng Z, Xiao Y, Xiao L, Tran NK, Phan-Le DP, Nguyen HD, Liu X, Hu J, Huang M, Liang J, Feng C, Zhang X, Tong L, Du B, Pham HH, Nguyen TH, Xu M, Jiang J, Zhang J, Liu Y, Hasan MK, Gan J, Liang Z, Cai W, Huang Y, Luo G, Yaqub M, Lekadir K
Source: IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2026 Jun; Vol. 45 (6), pp. 2950-2960.
Publication Type: Journal Article
Journal Info: Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 8310780 Publication Model: Print Cited Medium: Internet ISSN: 1558-254X (Electronic) Linking ISSN: 02780062 NLM ISO Abbreviation: IEEE Trans Med Imaging Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1558-254X
DOI:10.1109/TMI.2026.3666364