The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
about
Crowdsourcing and Automated Retinal Image Analysis for Diabetic Retinopathy.Comparing Amazon's Mechanical Turk Platform to Conventional Data Collection Methods in the Health and Medical Research Literature.Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review
P2860
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
description
2016 nî lūn-bûn
@nan
2016 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2016 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
name
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@ast
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@en
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@nl
type
label
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@ast
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@en
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@nl
prefLabel
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@ast
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@en
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@nl
P2093
P2860
P50
P3181
P356
P1476
The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images
@en
P2093
1 for the UK Biobank Eye and Vision Consortium
Baljean Dhillon
Danny Mitry
Kris Zutis
Paul J Foster
Shabina Hayat
P2860
P3181
P356
10.1167/TVST.5.5.6
P577
2016-09-21T00:00:00Z