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dc.contributor.author
Chaitanya, Krishna
dc.contributor.author
Karani, Neerav
dc.contributor.author
Baumgartner, Christian F.
dc.contributor.author
Erdil, Ertunc
dc.contributor.author
Becker, Anton
dc.contributor.author
Donati, Olivio
dc.contributor.author
Konukoglu, Ender
dc.date.accessioned
2021-01-12T12:44:21Z
dc.date.available
2021-01-10T03:41:52Z
dc.date.available
2021-01-12T12:44:21Z
dc.date.issued
2021-02
dc.identifier.issn
1361-8415
dc.identifier.issn
1361-8423
dc.identifier.other
10.1016/j.media.2020.101934
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/460889
dc.identifier.doi
10.3929/ethz-b-000460889
dc.description.abstract
Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such datasets is not a favourable option because acquiring a large number of annotated samples from experts is time-consuming and expensive. Consequently, numerous methods have been proposed in the literature for learning with limited annotated examples. Unfortunately, the proposed approaches in the literature have not yet yielded significant gains over random data augmentation for image segmentation, where random augmentations themselves do not yield high accuracy. In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task. The generator of the proposed method models intensity and shape variations using two sets of transformations, as additive intensity transformations and deformation fields. Both transformations are optimized using labeled as well as unlabeled examples in a semi-supervised framework. Our experiments on three medical datasets, namely cardiac, prostate and pancreas, show that the proposed approach significantly outperforms standard augmentation and semi-supervised approaches for image segmentation in the limited annotation setting. The code is made publicly available at https://github.com/krishnabits001/task_driven_data_augmentation.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Data augmentation
en_US
dc.subject
Semi-supervised learning
en_US
dc.subject
Machine learning
en_US
dc.subject
Deep learning
en_US
dc.subject
Medical image segmentation
en_US
dc.title
Semi-supervised task-driven data augmentation for medical image segmentation
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-12-09
ethz.journal.title
Medical Image Analysis
ethz.journal.volume
68
en_US
ethz.journal.abbreviated
Med Image Anal
ethz.pages.start
101934
en_US
ethz.size
16 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::09579 - Konukoglu, Ender / Konukoglu, Ender
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02652 - Institut für Bildverarbeitung / Computer Vision Laboratory::09579 - Konukoglu, Ender / Konukoglu, Ender
ethz.date.deposited
2021-01-10T03:41:56Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-01-12T12:44:30Z
ethz.rosetta.lastUpdated
2022-03-29T04:48:15Z
ethz.rosetta.versionExported
true
ethz.COinS
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