Evaluation of Kidney Histological Images Using Unsupervised Deep Learning.

ABSTRACT:

Introduction

Evaluating histopathology via machine learning has gained research and clinical interest, and the performance of supervised learning tasks has been described in various areas of medicine. Unsupervised learning of histological images has the advantage of reproducibility for labeling; however, the relationship between unsupervised evaluation and clinical information remains unclear in nephrology.

Methods

We propose an unsupervised approach combining convolutional neural networks (CNNs) and a visualization algorithm to cluster the histological images and calculate the score for patients. We applied the approach to the entire images or patched images of the glomerulus of kidney biopsy samples stained with hematoxylin and eosin obtained from 68 patients with immunogl

SUBMITTER: Sato N 

PROVIDER: S-EPMC8418980 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature