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arXiv · 1606.01100

Learning under Distributed Weak Supervision

Martin Rajchl·Matthew C. H. Lee·Franklin Schrans·Alice Davidson·Jonathan Passerat-Palmbach·Giacomo Tarroni·Amir Alansary·Ozan Oktay·Bernhard Kainz·Daniel Rueckert
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Abstract

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 subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research.

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Martin Rajchl, Matthew C. H. Lee, Franklin Schrans, Alice Davidson, Jonathan Passerat-Palmbach, Giacomo Tarroni, Amir Alansary, Ozan Oktay, Bernhard Kainz, Daniel Rueckert. 2016-06-03. Learning under Distributed Weak Supervision. https://arxiv.org/abs/1606.01100

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