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Learning under Distributed Weak Supervision
[article]
2016
arXiv
pre-print
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are
arXiv:1606.01100v1
fatcat:rxu4kelptvf2bdrgv35dcsc5ey