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Learning and combining image neighborhoods using random forests for neonatal brain disease classifcation

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dc.contributor.author Zimmer, Veronika Anne
dc.contributor.author Glocker, Ben
dc.contributor.author Hahner, Nadine
dc.contributor.author Eixarch, Elisenda
dc.contributor.author Sanromà, Gerard
dc.contributor.author Gratacós Solsona, Eduard
dc.contributor.author Rueckert, Daniel
dc.contributor.author González Ballester, Miguel Ángel, 1973-
dc.contributor.author Piella Fenoy, Gemma
dc.date.accessioned 2019-03-25T14:07:45Z
dc.date.issued 2017
dc.identifier.citation Zimmer VA, Glocker B, Hahner N, Eixarch E, Sanroma G, Gratacós E, Rueckert D, González Ballester MA, Piella G. Learning and combining image neighborhoods using random forests for neonatal brain disease classification. Medical Image Analysis. 2017 Dec; 42:189-99. DOI: 10.1016/j.media.2017.08.004
dc.identifier.issn 1361-8415
dc.identifier.uri http://hdl.handle.net/10230/36958
dc.description.abstract It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensional representation of the data, while preserving all relevant information. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure and it is highly application dependent. The recently proposed neighborhood approximation forests learn a neighborhood structure in a dataset based on a user-defied distance. We propose a framework to learn multiple pairwise distances in a population of brain images and to combine them in an unsupervised manner optimally in a manifold learning step. Unlike other methods that only use a univariate distance measure, our method allows for a natural combination of multiple distances from heterogeneous sources. As a result, it yields a representation of the population that preserves the multiple distances. Furthermore, our method also selects the most predictive features associated with the distances. We evaluate our method in neonatal magnetic resonance images of three groups (term controls, patients affected by intrauterine growth restriction and mild isolated ventriculomegaly). We show that combining multiple distances related to the condition improves the overall characterization and classifcation of the three clinical groups compared to the use of single distances and classical unsupervised manifold learning.
dc.description.sponsorship V. A. Zimmer is supported by the grant FI-DGR 2013 (2013 FI B00159) from the Generalitat de Catalunya. This research was partially funded by the Spanish Ministry of Economy and Competitiveness (TIN2012-35874). This study was also supported by Instituto de Salud Carlos III (PI16/00861), integrated in the Plan Nacional de I+D+I and co-financed by ISCIII-Subdirección General de Evaluación and Fondo Europeo de Desarrollo Regional (FEDER) \Una manera de hacer Europa"; additionally, the research leading to these results has received funding from \la Caixa" Foundation.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Medical Image Analysis. 2017 Dec; 42:189-99
dc.rights © Elsevier http://dx.doi.org/10.1016/j.media.2017.08.004
dc.title Learning and combining image neighborhoods using random forests for neonatal brain disease classifcation
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.media.2017.08.004
dc.subject.keyword Random forest
dc.subject.keyword Neighborhood approximation forest
dc.subject.keyword Manifold learning
dc.subject.keyword Similarity measure
dc.subject.keyword Brain development
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


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