CLU-CNNs: Object detection for medical images

Li, Z; Dong, M; Wen, S; Hu, X; Zhou, P; Zeng, Z

CLU-CNNs: Object detection for medical images

Li, Z Dong, M Wen, S

Hu, X Zhou, P Zeng, Z

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Neurocomputing, 2019, 350, pp. 53-59
Issue Date:: 2019-07-20

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Field	Value	Language
dc.contributor.author	Li, Z
dc.contributor.author	Dong, M
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Hu, X
dc.contributor.author	Zhou, P
dc.contributor.author	Zeng, Z
dc.date.accessioned	2022-07-18T06:14:40Z
dc.date.available	2022-07-18T06:14:40Z
dc.date.issued	2019-07-20
dc.identifier.citation	Neurocomputing, 2019, 350, pp. 53-59
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/159004
dc.description.abstract	Medical images have different characteristics from normal images. As an important feature, there usually exists data distribution difference between source domain and target domain for data scarcity and privacy. In this paper, a domain adaptation framework called CLU-CNNs is proposed, which is designed for medical images. CLU-CNNs uses ANCF and BN-IN Net to improve domain adaptation capability without specific domain adaptation training. Based on probability distribution assumptions of networks’ output, ANCF is a new path for domain adaptation. And BN-IN Net is embedded in fully convolutional networks to improve stability. This work has three key contributions: (1) A new object detection domain adaptation method is proposed in this paper without specific domain adaptation training. (2) Designed for medical images, CLU-CNNs performs well on small dataset, and is easy to be expanded. (3) CLU-CNNs obtains high positioning accuracy and fast speed when there is data distribution difference between source domain and target domain. Test on REFUGE CHALLENGE 2018, our way achieves state of the art performance.
dc.language	English
dc.publisher	Elsevier
dc.relation	National Natural Science Foundation of China61673187
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2019.04.028
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	CLU-CNNs: Object detection for medical images
dc.type	Journal Article
utslib.citation.volume	350
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-07-18T06:14:39Z
pubs.publication-status	Published
pubs.volume	350

Abstract:

Medical images have different characteristics from normal images. As an important feature, there usually exists data distribution difference between source domain and target domain for data scarcity and privacy. In this paper, a domain adaptation framework called CLU-CNNs is proposed, which is designed for medical images. CLU-CNNs uses ANCF and BN-IN Net to improve domain adaptation capability without specific domain adaptation training. Based on probability distribution assumptions of networks’ output, ANCF is a new path for domain adaptation. And BN-IN Net is embedded in fully convolutional networks to improve stability. This work has three key contributions: (1) A new object detection domain adaptation method is proposed in this paper without specific domain adaptation training. (2) Designed for medical images, CLU-CNNs performs well on small dataset, and is easy to be expanded. (3) CLU-CNNs obtains high positioning accuracy and fast speed when there is data distribution difference between source domain and target domain. Test on REFUGE CHALLENGE 2018, our way achieves state of the art performance.

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http://hdl.handle.net/10453/159004