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Data for glomeruli characterization in histopathological images
The data presented in this article is part of the whole slide imaging (WSI) datasets generated in European project AIDPATH This data is also related to the research paper entitle “Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation”, published in Computer Methods and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7058889/ https://www.ncbi.nlm.nih.gov/pubmed/32154349 http://dx.doi.org/10.1016/j.dib.2020.105314 |
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author | Bueno, Gloria Gonzalez-Lopez, Lucia Garcia-Rojo, Marcial Laurinavicius, Arvydas Deniz, Oscar |
author_facet | Bueno, Gloria Gonzalez-Lopez, Lucia Garcia-Rojo, Marcial Laurinavicius, Arvydas Deniz, Oscar |
author_sort | Bueno, Gloria |
collection | PubMed |
description | The data presented in this article is part of the whole slide imaging (WSI) datasets generated in European project AIDPATH This data is also related to the research paper entitle “Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation”, published in Computer Methods and Programs in Biomedicine Journal [1]. In that article, different methods based on deep learning for glomeruli segmentation and their classification into normal and sclerotic glomerulous are presented and discussed. The raw data used is described and provided here. In addition, the detected glomeruli are also provided as individual image files. These data will encourage research on artificial intelligence (AI) methods, create and compare fresh algorithms, and measure their usability in quantitative nephropathology. |
format | Online Article Text |
id | pubmed-7058889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-70588892020-03-09 Data for glomeruli characterization in histopathological images Bueno, Gloria Gonzalez-Lopez, Lucia Garcia-Rojo, Marcial Laurinavicius, Arvydas Deniz, Oscar Data Brief Medicine and Dentistry The data presented in this article is part of the whole slide imaging (WSI) datasets generated in European project AIDPATH This data is also related to the research paper entitle “Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation”, published in Computer Methods and Programs in Biomedicine Journal [1]. In that article, different methods based on deep learning for glomeruli segmentation and their classification into normal and sclerotic glomerulous are presented and discussed. The raw data used is described and provided here. In addition, the detected glomeruli are also provided as individual image files. These data will encourage research on artificial intelligence (AI) methods, create and compare fresh algorithms, and measure their usability in quantitative nephropathology. Elsevier 2020-02-24 /pmc/articles/PMC7058889/ /pubmed/32154349 http://dx.doi.org/10.1016/j.dib.2020.105314 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Medicine and Dentistry Bueno, Gloria Gonzalez-Lopez, Lucia Garcia-Rojo, Marcial Laurinavicius, Arvydas Deniz, Oscar Data for glomeruli characterization in histopathological images |
title | Data for glomeruli characterization in histopathological images |
title_full | Data for glomeruli characterization in histopathological images |
title_fullStr | Data for glomeruli characterization in histopathological images |
title_full_unstemmed | Data for glomeruli characterization in histopathological images |
title_short | Data for glomeruli characterization in histopathological images |
title_sort | data for glomeruli characterization in histopathological images |
topic | Medicine and Dentistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7058889/ https://www.ncbi.nlm.nih.gov/pubmed/32154349 http://dx.doi.org/10.1016/j.dib.2020.105314 |
work_keys_str_mv | AT buenogloria dataforglomerulicharacterizationinhistopathologicalimages AT gonzalezlopezlucia dataforglomerulicharacterizationinhistopathologicalimages AT garciarojomarcial dataforglomerulicharacterizationinhistopathologicalimages AT laurinaviciusarvydas dataforglomerulicharacterizationinhistopathologicalimages AT denizoscar dataforglomerulicharacterizationinhistopathologicalimages |