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Data for glomeruli characterization in histopathological images
- Source :
- Data in Brief, Vol 29, Iss, Pp-(2020), Data in brief, Amsterdam : Elsevier, 2020, vol. 29, art. no. 105314, p. [1-5], Data in Brief
- Publication Year :
- 2020
- Publisher :
- Elsevier, 2020.
-
Abstract
- The data presented in this article is part of the whole slide imaging (WSI) datasets generated in European project AIDPATH 2 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. Keywords: Glomeruli identification, Normal glomerulus, Global sclerotic glomerulus, Whole slide image, Digital pathology
- Subjects :
- Computer science
Whole slide image
Global sclerotic glomerulus
lcsh:Computer applications to medicine. Medical informatics
03 medical and health sciences
0302 clinical medicine
Normal glomerulus
Digital pathology
Segmentation
lcsh:Science (General)
030304 developmental biology
0303 health sciences
Measure (data warehouse)
Multidisciplinary
business.industry
Deep learning
Pattern recognition
Usability
computer.file_format
Medicine and Dentistry
Identification (information)
digital pathology
global sclerotic glomerulus
glomeruli identification
normal glomerulus
whole slide image
lcsh:R858-859.7
Image file formats
Artificial intelligence
Raw data
business
Glomeruli identification
computer
030217 neurology & neurosurgery
lcsh:Q1-390
Subjects
Details
- Language :
- English
- ISSN :
- 23523409
- Volume :
- 29
- Database :
- OpenAIRE
- Journal :
- Data in Brief
- Accession number :
- edsair.doi.dedup.....b7e2b1e022806adddfe226e9a2e471ae