Group-constrained Laplacian Eigenmaps: Longitudinal AD biomarker learning
Guerrero R, Ledig C, Schmidt-Richberg A, Rueckert D (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9352
Pages Range: 178-185
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783319248875
DOI: 10.1007/978-3-319-24888-2_22
Abstract
Longitudinal modeling of biomarkers to assess a subject’s risk of developing Alzheimers disease (AD) or determine the current state in the disease trajectory has recently received increased attention. Here, a new method to estimate the time-to-conversion (TTC) of mild cognitive impaired (MCI) subjects to AD from a low-dimensional representation of the data is proposed. This is achieved via a combination of multi-level feature selection followed by a novel formulation of the Laplacian Eigenmaps manifold learning algorithm that allows the incorporation of group constraints. Feature selection is performed using Magnetic Resonance (MR) images that have been aligned at different detail levels to a template. The suggested group constraints are added to the construction of the neighborhood matrix which is used to calculate the graph Laplacian in the Laplacian Eigenmaps algorithm. The proposed formulation yields relevant improvements for the prediction of the TTC and for the three-way classification (control/MCI/AD) on the ADNI database.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Guerrero, R., Ledig, C., Schmidt-Richberg, A., & Rueckert, D. (2015). Group-constrained Laplacian Eigenmaps: Longitudinal AD biomarker learning. In Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 178-185). Munich, DEU: Springer Verlag.
MLA:
Guerrero, R., et al. "Group-constrained Laplacian Eigenmaps: Longitudinal AD biomarker learning." Proceedings of the 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015, Munich, DEU Ed. Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang, Springer Verlag, 2015. 178-185.
BibTeX: Download