ArticleData in brief2020
Data for glomeruli characterization in histopathological images.
Article in Data in brief, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- StainStyleSampler: clustering-based sampling of whole slide image appearances.Diagnostic pathology · 2026Article
- Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications.Scientific reports · 2025Article
- GNCnn: A QuPath extension for glomerulosclerosis and glomerulonephritis characterization based on deep learning.Computational and structural biotechnology journal · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- An image inpainting-based data augmentation method for improved sclerosed glomerular identification performance with the segmentation model EfficientNetB3-Unet.Scientific reports · 2024Article
- Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.Bioinformatics (Oxford, England) · 2024Article
- Segmentation of human functional tissue units in support of a Human Reference Atlas.Communications biology · 2023Article
- Artificial intelligence in renal pathology: Current status and future.Biomolecules & biomedicine · 2023Review
- Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Holistic fine-grained global glomerulosclerosis characterization: from detection to unbalanced classification.Journal of medical imaging (Bellingham, Wash.) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.