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A comprehensive review of U-Net architectures for medical image segmentation: emerging trends and federated learning perspectives.

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Title: A comprehensive review of U-Net architectures for medical image segmentation: emerging trends and federated learning perspectives.
Authors: Hossen, Md. Fahim1, 2110055@student.ruet.ac.bd, Sarkar, Anirban1, 2110060@student.ruet.ac.bd, Majumder, Turja1, 2110017@student.ruet.ac.bd, Nabi, Md. Fahmidun1, 2110025@student.ruet.ac.bd, Ahamed, Md. Faysal1, faysal.ahamed@ece.ruet.ac.bd, Shafi, Fariya Bintay1, 1710027@student.ruet.ac.bd, Sarmun, Rusab2, rusab-2016315002@eee.du.ac.bd, Chowdhury, Muhammad E. H.2, mchowdhury@qu.edu.qa
Source: Neural Computing & Applications; Jun2026, Vol. 38 Issue 12, p1-53, 53p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 194652010
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: A comprehensive review of U-Net architectures for medical image segmentation: emerging trends and federated learning perspectives.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=194652010
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      – Type: doi
        Value: 10.1007/s00521-026-12198-6
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      – Code: eng
        Text: English
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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