Assessment of glomerular morphological patterns by deep learning algorithms.

Saved in:
Bibliographic Details
Title: Assessment of glomerular morphological patterns by deep learning algorithms.
Authors: Weis CA; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany. cleo-aron.weis@medma.uni-heidelberg.de., Bindzus JN; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany., Voigt J; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany., Runz M; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.; Mannheim Institute for Intelligent Systems in Medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany., Hertjens S; Institute of Medical Statistics and Biometry, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany., Gaida MM; Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany., Popovic ZV; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany., Porubsky S; Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany. stefan.porubsky@unimedizin-mainz.de.
Source: Journal of nephrology [J Nephrol] 2022 Mar; Vol. 35 (2), pp. 417-427. Date of Electronic Publication: 2022 Jan 04.
Publication Type: Journal Article
Journal Info: Publisher: Springer Country of Publication: Italy NLM ID: 9012268 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1724-6059 (Electronic) Linking ISSN: 11218428 NLM ISO Abbreviation: J Nephrol Subsets: MEDLINE
Database: MEDLINE Ultimate
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 34982414
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Assessment of glomerular morphological patterns by deep learning algorithms.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Weis+CA%22">Weis CA</searchLink>; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany. cleo-aron.weis@medma.uni-heidelberg.de.<br /><searchLink fieldCode="AU" term="%22Bindzus+JN%22">Bindzus JN</searchLink>; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.<br /><searchLink fieldCode="AU" term="%22Voigt+J%22">Voigt J</searchLink>; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.<br /><searchLink fieldCode="AU" term="%22Runz+M%22">Runz M</searchLink>; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.; Mannheim Institute for Intelligent Systems in Medicine, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany.<br /><searchLink fieldCode="AU" term="%22Hertjens+S%22">Hertjens S</searchLink>; Institute of Medical Statistics and Biometry, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.<br /><searchLink fieldCode="AU" term="%22Gaida+MM%22">Gaida MM</searchLink>; Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany.<br /><searchLink fieldCode="AU" term="%22Popovic+ZV%22">Popovic ZV</searchLink>; Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, 68167, Mannheim, Germany.<br /><searchLink fieldCode="AU" term="%22Porubsky+S%22">Porubsky S</searchLink>; Institute of Pathology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany. stefan.porubsky@unimedizin-mainz.de.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%229012268%22">Journal of nephrology</searchLink> [J Nephrol] 2022 Mar; Vol. 35 (2), pp. 417-427. <i>Date of Electronic Publication: </i>2022 Jan 04.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Springer%22">Springer </searchLink><i>Country of Publication: </i>Italy <i>NLM ID: </i>9012268 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1724-6059 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2211218428%22">11218428 </searchLink><i>NLM ISO Abbreviation: </i>J Nephrol <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=34982414
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s40620-021-01221-9
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 417
    Titles:
      – TitleFull: Assessment of glomerular morphological patterns by deep learning algorithms.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Weis CA
      – PersonEntity:
          Name:
            NameFull: Bindzus JN
      – PersonEntity:
          Name:
            NameFull: Voigt J
      – PersonEntity:
          Name:
            NameFull: Runz M
      – PersonEntity:
          Name:
            NameFull: Hertjens S
      – PersonEntity:
          Name:
            NameFull: Gaida MM
      – PersonEntity:
          Name:
            NameFull: Popovic ZV
      – PersonEntity:
          Name:
            NameFull: Porubsky S
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: 2022 Mar
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-electronic
              Value: 1724-6059
          Numbering:
            – Type: volume
              Value: 35
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Journal of nephrology
              Type: main
ResultId 1