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Learning under Distributed Weak Supervision

Rajchl, M., Lee, M., Schrans, F., Davidson, A., Passerat-Palmbach, J., Tarroni, G. ORCID: 0000-0002-0341-6138, Alansary, A., Oktay, O., Kainz, B. and Rueckert, D. (2020). Learning under Distributed Weak Supervision. .

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.

Publication Type: Monograph (Working Paper)
Additional Information: ┬ęthe authors, 2020.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
URI: https://openaccess.city.ac.uk/id/eprint/23502
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