Learning contextual and attentive information for brain tumor segmentation

Zhou, C; Chen, S; Ding, C; Tao, D

Learning contextual and attentive information for brain tumor segmentation

Zhou, C Chen, S Ding, C Tao, D

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11384 LNCS, pp. 497-507
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Zhou, C
dc.contributor.author	Chen, S
dc.contributor.author	Ding, C
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449
dc.date.accessioned	2020-06-16T00:37:14Z
dc.date.available	2020-06-16T00:37:14Z
dc.date.issued	2019-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11384 LNCS, pp. 497-507
dc.identifier.isbn	9783030117252
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/141439
dc.description.abstract	© Springer Nature Switzerland AG 2019. Thanks to the powerful representation learning ability, convolutional neural network has been an effective tool for the brain tumor segmentation task. In this work, we design multiple deep architectures of varied structures to learning contextual and attentive information, then ensemble the predictions of these models to obtain more robust segmentation results. In this way, the risk of overfitting in segmentation is reduced. Experimental results on validation dataset of BraTS 2018 challenge demonstrate that the proposed method can achieve good performance with average Dice scores of 0.8136, 0.9095 and 0.8651 for enhancing tumor, whole tumor and tumor core, respectively. The corresponding scores for BraTS 2018 testing set are 0.7775, 0.8842 and 0.7960, respectively, winning the third position in the BraTS 2018 competition among 64 participating teams.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-11726-9_44
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Learning contextual and attentive information for brain tumor segmentation
dc.type	Conference Proceeding
utslib.citation.volume	11384 LNCS
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-06-16T00:37:08Z
pubs.publication-status	Published
pubs.volume	11384 LNCS

Abstract:

© Springer Nature Switzerland AG 2019. Thanks to the powerful representation learning ability, convolutional neural network has been an effective tool for the brain tumor segmentation task. In this work, we design multiple deep architectures of varied structures to learning contextual and attentive information, then ensemble the predictions of these models to obtain more robust segmentation results. In this way, the risk of overfitting in segmentation is reduced. Experimental results on validation dataset of BraTS 2018 challenge demonstrate that the proposed method can achieve good performance with average Dice scores of 0.8136, 0.9095 and 0.8651 for enhancing tumor, whole tumor and tumor core, respectively. The corresponding scores for BraTS 2018 testing set are 0.7775, 0.8842 and 0.7960, respectively, winning the third position in the BraTS 2018 competition among 64 participating teams.

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http://hdl.handle.net/10453/141439