Glomerular Classification Using Convolutional Neural Networks Based on Defined Annotation Criteria and Concordance Evaluation Among Clinicians.

Introduction: Diagnosing renal pathologies is important for performing treatments. However, classifying every glomerulus is difficult for clinicians; thus, a support system, such as a computer, is required. This paper describes the automatic classification of glomerular images using a convolutional neural network (CNN).
Method: To generate appropriate labeled data, annotation criteria including 12 features (e.g., "fibrous crescent") were defined. The concordance among 5 clinicians was evaluated for 100 images using the kappa (κ) coefficient for each feature. Using the annotation criteria, 1 clinician annotated 10,102 images. We trained the CNNs to classify the features with an average κ ≥0.4 and evaluated their performance using the receiver operating characteristic-area under the curve (ROC-AUC). An error analysis was conducted and the gradient-weighted class activation mapping (Grad-CAM) was also applied; it expresses the CNN's focusing point with a heat map when the CNN classifies the glomerular image for a feature.
Results: The average κ coefficient of the features ranged from 0.28 to 0.50. The ROC-AUC of the CNNs for test data varied from 0.65 to 0.98. Among the features, "capillary collapse" and "fibrous crescent" had high ROC-AUC values of 0.98 and 0.91, respectively. The error analysis and the Grad-CAM visually showed that the CNN could not distinguish between 2 different features that had similar visual structures or that occurred simultaneously.
Conclusion: The differences in the texture or frequency of the co-occurrence between the different features affected the CNN performance; thus, to improve the classification accuracy, methods such as segmentation are required.
(© 2020 International Society of Nephrology. Published by Elsevier Inc.)