TY - GEN
T1 - Group-constrained Laplacian Eigenmaps
T2 - 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
AU - Guerrero, R.
AU - Ledig, C.
AU - Schmidt-Richberg, A.
AU - Rueckert, D.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84951924142&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24888-2_22
DO - 10.1007/978-3-319-24888-2_22
M3 - Conference contribution
AN - SCOPUS:84951924142
SN - 9783319248875
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 178
EP - 185
BT - Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
A2 - Zhou, Luping
A2 - Shi, Yinghuan
A2 - Wang, Li
A2 - Wang, Qian
PB - Springer Verlag
Y2 - 5 October 2015 through 5 October 2015
ER -