AIMC Topic: Kidney Glomerulus
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Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications.
Scientific reports
Jul 2, 2025
Kidney pathology of immunoglobulin A nephropathy (IgAN), which is the key finding of both diagnosis and risk stratification, involves labor-intensive manual interpretation as well as unavoidable interpreter-dependent variabilities. We propose artific...
AI-driven glomerular morphology quantification: a novel pipeline for assessing basement membrane thickness and podocyte foot process effacement in kidney diseases.
Computer methods and programs in biomedicine
May 7, 2025
BACKGROUND AND OBJECTIVE: Measuring the thickness of the glomerular basement membrane (GBM) and assessing the percentage of podocyte foot process effacement (%PFPE) are important for diagnosing non-neoplastic kidney diseases. However, when performed ...
Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy.
Journal of clinical pathology
Jan 17, 2025
BACKGROUND: Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset o...
Exploring the subtle and novel renal pathological changes in diabetic nephropathy using clustering analysis with deep learning.
Scientific reports
Jan 15, 2025
To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease is required. We performed invariant information clustering (IIC)-based clustering on glomerular images obtained from nephrectomized kidneys of patients...
Glo-net: A dual task branch based neural network for multi-class glomeruli segmentation.
Computers in biology and medicine
Jan 11, 2025
Accurate segmentation and classification of glomeruli are fundamental to histopathology slide analysis in renal pathology, which helps to characterize individual kidney disease. Accurate segmentation of glomeruli of different types faces two main cha...
Unveiling pathology-related predictive uncertainty of glomerular lesion recognition using prototype learning.
Journal of biomedical informatics
Dec 31, 2024
OBJECTIVE: Recognizing glomerular lesions is essential in diagnosing chronic kidney disease. However, deep learning faces challenges due to the lesion heterogeneity, superposition, progression, and tissue incompleteness, leading to uncertainty in mod...
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 ...
Accurate classification of glomerular diseases by hyperspectral imaging and transformer.
Computer methods and programs in biomedicine
Jun 11, 2024
BACKGROUND AND OBJECTIVE: In renal disease research, precise glomerular disease diagnosis is crucial for treatment and prognosis. Currently reliant on invasive biopsies, this method bears risks and pathologist-dependent variability, yielding inconsis...
Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.
International journal of computer assisted radiology and surgery
Jun 7, 2024
PURPOSE: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficul...
Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy.
Journal of translational medicine
Apr 29, 2024
BACKGROUND: Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consumin...
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