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dc.contributor.authorLiu, Sidong
dc.contributor.authorCai, Weidong
dc.contributor.authorLiu, Siqi
dc.contributor.authorPujol, Sonia
dc.contributor.authorKikinis, Ron
dc.contributor.authorFeng, Dagan
dc.date.accessioned2019-06-11
dc.date.available2019-06-11
dc.date.issued2015-12-10
dc.identifier.citationS. Liu, W. Cai, S. Liu, S. Pujol, R. Kikinis and D. Feng, "Subject-centered multi-view feature fusion for neuroimaging retrieval and classification," 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, 2015, pp. 2505-2509. doi: 10.1109/ICIP.2015.7351253en_AU
dc.identifier.isbn978-1-4799-8339-1
dc.identifier.urihttp://hdl.handle.net/2123/20515
dc.description.abstractMulti-View neuroimaging retrieval and classification play an important role in computer-aided-diagnosis of brain disorders, as multi-view features could provide more insights of the disease pathology and potentially lead to more accurate diagnosis than single-view features. The large inter-feature and inter-subject variations make the multi-view neuroimaging analysis a challenging task. Many multi-view or multi-modal feature fusion methods have been proposed to reduce the impact of inter-feature variations in neuroimaging data. However, there is not much in-depth work focusing on the inter-subject variations. In this study, we propose a subject-centered multi-view feature fusion method for neuroimaging retrieval and classification based on the propagation graph fusion (PGF) algorithm. Two main advantages of the proposed method are: 1) it evaluates the query online and adaptively reshapes the connections between subjects according to the query; 2) it measures the affinity of the query to the subjects using the subject-centered affinity matrices, which can be easily combined and efficiently solved. Evaluated using a public accessible neuroimaging database, our algorithm outperforms the state-of-the-art methods in retrieval and achieves comparable performance in classification.en_AU
dc.publisherIEEEen_AU
dc.relationARC DP140100211
dc.rights© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_AU
dc.titleSubject-centered multi-view feature fusion for neuroimaging retrieval and classificationen_AU
dc.typeConference paperen_AU
dc.type.pubtypePost-printen_AU


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