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Updated: Jun 11, 2025
Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal
Masatoshi Abe1, Hirohiko Niioka2, Ayumi Matsumoto1
1Department of Nephrology, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, Japan.
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 due to the specialized knowledge and time required for their creation. This limitation hinders the widespread application of deep learning for the analysis of kidney biopsy images.
Methods:
We applied self-distillation with no labels (DINO), a self-supervised learning method, to a dataset of 10,423 glomerular images obtained from 384 PAS-stained kidney biopsy slides. Glomerular features extracted from the DINO-pretrained backbone were visualized using principal component analysis (PCA). We then performed classification tasks by adding either k-nearest neighbor (kNN) classifiers or linear head layers to the DINO-pretrained or ImageNet-pretrained backbones. These models were trained on our labeled classification dataset. Performance was evaluated using metrics such as the area under the receiver operating characteristic curve (ROC-AUC). The classification tasks encompassed four disease categories (minimal change disease, mesangial proliferative glomerulonephritis, membranous nephropathy, and diabetic nephropathy) as well as clinical parameters such as hypertension, proteinuria, and hematuria.
Results:
PCA visualization revealed distinct principal components corresponding to different glomerular structures, demonstrating the capability of the DINO-pretrained backbone to capture morphological features. In disease classification, the DINO-pretrained transferred model (ROC-AUC = 0.93) outperformed the ImageNet-pretrained fine-tuned model (ROC-AUC = 0.89). When the labeled data were limited, the ImageNet-pretrained fine-tuned model's ROC-AUC dropped to 0.76 (95% confidence interval [CI], 0.72-0.80), whereas the DINO-pretrained transferred model maintained superior performance (ROC-AUC 0.88, 95% CI 0.86-0.90). The DINO-pretrained transferred model also exhibited higher AUCs for the classification of several clinical parameters. External validation using two independent datasets confirmed DINO pre-training's superiority, particularly when labeled data were limited.
Conclusions:
The application of DINO to unlabeled PAS-stained glomerular images facilitated the extraction of histological features that can be effectively utilized for disease classification.
