Unsupervised machine learning for identifying important visual features through bag-of-words using h...
Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease
About this item
Full title
Author / Creator
C-PROBE Study , the C-PROBE Study , Lee, Joonsang , Warner, Elisa , Shaikhouni, Salma , Bitzer, Markus , Kretzler, Matthias , Gipson, Debbie , Pennathur, Subramaniam , Bellovich, Keith , Bhat, Zeenat , Gadegbeku, Crystal , Massengill, Susan , Perumal, Kalyani , Saha, Jharna , Yang, Yingbao , Luo, Jinghui , Zhang, Xin , Mariani, Laura , Hodgin, Jeffrey B. and Rao, Arvind
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver...
Alternative Titles
Full title
Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease
Authors, Artists and Contributors
Author / Creator
the C-PROBE Study
Lee, Joonsang
Warner, Elisa
Shaikhouni, Salma
Bitzer, Markus
Kretzler, Matthias
Gipson, Debbie
Pennathur, Subramaniam
Bellovich, Keith
Bhat, Zeenat
Gadegbeku, Crystal
Massengill, Susan
Perumal, Kalyani
Saha, Jharna
Yang, Yingbao
Luo, Jinghui
Zhang, Xin
Mariani, Laura
Hodgin, Jeffrey B.
Rao, Arvind
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_fe2e0a13ca2544f7bdcbdd8b50cb682f
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_fe2e0a13ca2544f7bdcbdd8b50cb682f
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-022-08974-8
