Can I read Glomerular Disease Classification and Lesion Identification by Machine Learning on EtoBox?

Glomerular Disease Classification and Lesion Identification by Machine Learning by Cheng-Kun Yang; Ching-Yi Lee; Hsiang-Sheng Wang; Shun-Chen Huang; Peir-In Liang; Jung-Sheng Chen; Chang-Fu Kuo; Kun-Hua Tu; Chao-Yuan Yeh; Tai-Di Chen is a Medicine article available to read on EtoBox.

What is Glomerular Disease Classification and Lesion Identification by Machine Learning about?

## Background Classification of glomerular diseases and identification of glomerular lesions require careful morphological examination by experienced nephropathologists, which is labor-intensive, time-consuming, and prone to interobserver variability. In this regard, recent advance in machine learning-based image analysis is promising. ## Methods We combined Mask Region-based Convolutional Neural Networks (Mask R–CNN) with an additional classification step to build a glomerulus detection model using human kidney biopsy samples. A Long Short-Term Memory (LSTM) recurrent neural network was applied for glomerular disease classification, and another two-stage model using ResNeXt-101 was constructed for glomerular lesion identification in cases of lupus nephritis. ## Results The detection model showed state-of-the-art performance on variedly stained slides with F1 scores up to 0.944. The disease classification model showed good accuracies up to 0.940 on recognizing different glomerular diseases based on H&E whole slide images. The lesion identification model demonstrated high discriminating power with area under the receiver operating characteristic curve up to 0.947 for various glomeru

Who reads Glomerular Disease Classification and Lesion Identification by Machine Learning?

Author

Cheng-Kun Yang; Ching-Yi Lee; Hsiang-Sheng Wang; Shun-Chen Huang; Peir-In Liang; Jung-Sheng Chen; Chang-Fu Kuo; Kun-Hua Tu; Chao-Yuan Yeh; Tai-Di Chen

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Medicine (Life Sciences)