Learning and combining image similarities for neonatal brain population studies
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
The characterization of neurodevelopment is challenging due to the complex structural changes of the brain in early childhood. To analyze the changes in a population across time and to relate them with clinical information, manifold learning techniques can be applied. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure in the embedding and highly application dependent. It has been shown that the combination of several notions of similarity and features can improve the new representation. However, how to combine and weight different similarites and features is non-trivial. In this work, we propose to learn the neighborhood structure and similarity measure used for manifold learning through Neighborhood Approximation Forests (NAFs). The recently proposed NAFs learn a neighborhood structure in a dataset based on a user-defined distance. A characterization of image similarity using NAFs enables us to construct manifold representations based on a previously defined criterion to improve predictions regarding structural and clinical information. In particular, NAFs can be used naturally to combine the affinities learned from multiple distances in a joint manifold towards a more meaningful representation and an improved characterization of the resulting embedding. We demonstrate the utility of NAFs in manifold learning on a population of preterm and in term neonates for classification regarding structural volume and clinical information.
Date Issued
2015-10-02
Date Acceptance
2015-08-01
Citation
Lecture Notes in Computer Science, 2015, 9352, pp.110-117
ISBN
978-3-319-24887-5
ISSN
0302-9743
Publisher
Springer International Publishing
Start Page
110
End Page
117
Journal / Book Title
Lecture Notes in Computer Science
Volume
9352
Copyright Statement
© 2015 Springer International Publishing Switzerland. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-24888-2_14
Source
International Workshop on Machine Learning in Medical Imaging (MLMI)
Publication Status
Published
Start Date
2015-10-05
Coverage Spatial
Munich, Germany