当前位置： SCI文献检索 > Scientific Reports期刊下所有文献 > A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides.

A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides.

Abstract：

:Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of renal allograft rejection. A fully automated system using a convolutional neural network (CNN) was developed to identify regions of interest (ROIs) and to detect C4d positive and negative peritubular capillaries (PTCs) in giga-pixel immunostained slides. The performance of faster R-CNN was evaluated using optimal parameters of the novel method to enlarge the size of labeled masks. Fifty and forty pixels of the enlarged size images showed the best performance in detecting C4d positive and negative PTCs, respectively. Additionally, the feasibility of deep-learning-assisted labeling as independent dataset to enhance detection in this model was evaluated. Based on these two CNN methods, a fully automated system for renal allograft rejection was developed. This system was highly reliable, efficient, and effective, making it applicable to real clinical workflow.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Kim YG,Choi G,Go H,Cho Y,Lee H,Lee AR,Park B,Kim N

doi

10.1038/s41598-019-41479-5

subject

Has Abstract

pub_date

2019-03-26 00:00:00

pages

5123

issue

1

issn

2045-2322

pii

10.1038/s41598-019-41479-5

journal_volume

9

pub_type

杂志文章

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