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

ABSTRACT:

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

SUBMITTER: Yamaguchi R 

PROVIDER: S-EPMC7938073 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature