AIMC Topic: Nephrosis, Lipoid
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Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.
Journal of the American Society of Nephrology : JASN
Oct 9, 2024
BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required ...
Screening core genes for minimal change disease based on bioinformatics and machine learning approaches.
International urology and nephrology
Oct 9, 2024
Based on bioinformatics and machine learning methods, we conducted a study to screen the core genes of minimal change disease (MCD) and further explore its pathogenesis. First, we obtained the chip data sets GSE108113 and GSE200828 from the Gene Expr...
Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification.
Kidney360
Dec 9, 2021
Pathologists use multiple microscopy modalities to assess renal biopsy specimens. Besides usual diagnostic features, some changes are too subtle to be properly defined. Computational approaches have the potential to systematically quantitate subvisua...
Clinical relevance of computationally derived tubular features and their spatial relationships with the interstitial microenvironment in minimal change disease/focal segmental glomerulosclerosis.
Kidney international
Aug 1, 2025
BACKGROUND: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. Here, we investigated if computationally quantified tubular features can enhance prognostication and reveal sp...
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