Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Rajchl:2016,
author = {Rajchl, M and Lee, M and Schrans, F and Davidson, A and Passerat-Palmbach, J and Tarroni, G and Alansary, A and Oktay, O and Kainz, B and Rueckert, D},
title = {Learning under Distributed Weak Supervision},
url = {http://arxiv.org/abs/1606.01100},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - 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.
AU  - Rajchl,M
AU  - Lee,M
AU  - Schrans,F
AU  - Davidson,A
AU  - Passerat-Palmbach,J
AU  - Tarroni,G
AU  - Alansary,A
AU  - Oktay,O
AU  - Kainz,B
AU  - Rueckert,D
PY  - 2016///
TI  - Learning under Distributed Weak Supervision
UR  - http://arxiv.org/abs/1606.01100
ER  -
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