A spatially guided machine learning method to classify and quantify glomerular patterns of injury in histology images

Author:

Besusparis Justinas1,Morkunas Mindaugas1,Laurinavicius Arvydas1

Affiliation:

1. Faculty of Medicine, Vilnius University, M.K.Ciurlionio 21, Vilnius, LT-03101

Abstract

Abstract INTRODUCTION Pathology diagnosis of glomerular diseases is primarily based on visual assessment of histologic patterns. Semi-quantitative scoring of active and chronic lesions is often required to assess individual characteristics of the disease. Reproducibility of the visual scoring systems remains debatable while digital and machine learning technologies open opportunities to detect, classify and quantify glomerular lesions, also considering their inter- and intraglomerular heterogeneity. MATERIALS AND METHODS We performed a cross-validated comparison of three modifications of a convolutional neural network (CNN)-based approach for recognition and intraglomerular quantification of nine main glomerular patterns of injury. Reference values provided by two nephropathologists were used for validation. For each glomerular image, visual attention heatmaps were generated with a probability of class attribution for further intraglomerular quantification. Quality of classifier-produced heatmaps was evaluated by an intersection over union metrics (IoU) between predicted and ground truth localization heatmaps. RESULTS A proposed spatially guided modification of CNN classifier achieved the highest glomerular pattern classification accuracies with AUC values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, spatially guided classifier achieved significantly higher generalized mean IoU value, compared with single-multiclass and multiple-binary classifiers. CONCLUSIONS We propose a spatially guided CNN classifier which in our experiments reveals the potential to achieve high accuracy for intraglomerular pattern localization.

Publisher

Research Square Platform LLC

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