Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd
about
Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational MethodDesigning image segmentation studies: Statistical power, sample size and reference standard quality.The wisdom of crowds for visual search.Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays.Mapping of Crowdsourcing in Health: Systematic Review.
P2860
Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology: evaluating experts, automated methods, and the crowd
description
2015 nî lūn-bûn
@nan
2015 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2015 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
name
Crowdsourcing image annotation ...... tomated methods, and the crowd
@ast
Crowdsourcing image annotation ...... tomated methods, and the crowd
@en
Crowdsourcing image annotation ...... tomated methods, and the crowd
@nl
type
label
Crowdsourcing image annotation ...... tomated methods, and the crowd
@ast
Crowdsourcing image annotation ...... tomated methods, and the crowd
@en
Crowdsourcing image annotation ...... tomated methods, and the crowd
@nl
prefLabel
Crowdsourcing image annotation ...... tomated methods, and the crowd
@ast
Crowdsourcing image annotation ...... tomated methods, and the crowd
@en
Crowdsourcing image annotation ...... tomated methods, and the crowd
@nl
P2093
P2860
P1476
Crowdsourcing image annotation ...... tomated methods, and the crowd
@en
P2093
L Montaser-Kouhsari
N W Knoblauch
P2860
P304
P356
10.1142/9789814644730_0029
P4510
P577
2015-01-01T00:00:00Z