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Data distillation in computational pathology by choosing few representants of the original variance: A use case in ovarian cancer.

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Title: Data distillation in computational pathology by choosing few representants of the original variance: A use case in ovarian cancer.
Authors: Salguero, Jennifer1, jsalguerol@unal.edu.co, Prasanna, Prateek2, prateek.prasanna@stonybrook.edu, Corredor, German3, german.corredor.prada@emory.edu, Cruz-Roa, Angel4, aacruz@unillanos.edu.co, Becerra, David1, Romero, Eduardo1, edromero@unal.edu.co
Source: Expert Systems with Applications; Jul2024, Vol. 245, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 176151950
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=176151950
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.eswa.2023.123028
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Titles:
      – TitleFull: Data distillation in computational pathology by choosing few representants of the original variance: A use case in ovarian cancer.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Salguero, Jennifer
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            NameFull: Prasanna, Prateek
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            NameFull: Corredor, German
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            NameFull: Cruz-Roa, Angel
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            NameFull: Becerra, David
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            NameFull: Romero, Eduardo
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          Dates:
            – D: 01
              M: 07
              Text: Jul2024
              Type: published
              Y: 2024
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              Value: 09574174
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              Value: 245
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            – TitleFull: Expert Systems with Applications
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