TY - GEN
T1 - Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classification
AU - Min, Rui
AU - Cheng, Jian
AU - Price, True
AU - Wu, Guorong
AU - Shen, Dinggang
PY - 2014
Y1 - 2014
N2 - In order to establish the correspondences between different brains for comparison, spatial normalization based morphometric measurements have been widely used in the analysis of Alzheimer's disease (AD). In the literature, different subjects are often compared in one atlas space, which may be insufficient in revealing complex brain changes. In this paper, instead of deploying one atlas for feature extraction and classification, we propose a maximum-margin based representation learning (MMRL) method to learn the optimal representation from multiple atlases. Unlike traditional methods that perform the representation learning separately from the classification, we propose to learn the new representation jointly with the classification model, which is more powerful in discriminating AD patients from normal controls (NC). We evaluated the proposed method on the ADNI database, and achieved 90.69% for AD/NC classification and 73.69% for p-MCI/s-MCI classification.
AB - In order to establish the correspondences between different brains for comparison, spatial normalization based morphometric measurements have been widely used in the analysis of Alzheimer's disease (AD). In the literature, different subjects are often compared in one atlas space, which may be insufficient in revealing complex brain changes. In this paper, instead of deploying one atlas for feature extraction and classification, we propose a maximum-margin based representation learning (MMRL) method to learn the optimal representation from multiple atlases. Unlike traditional methods that perform the representation learning separately from the classification, we propose to learn the new representation jointly with the classification model, which is more powerful in discriminating AD patients from normal controls (NC). We evaluated the proposed method on the ADNI database, and achieved 90.69% for AD/NC classification and 73.69% for p-MCI/s-MCI classification.
UR - http://www.scopus.com/inward/record.url?scp=84906979677&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10470-6_27
DO - 10.1007/978-3-319-10470-6_27
M3 - Conference contribution
AN - SCOPUS:84906979677
SN - 9783319104690
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 212
EP - 219
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PB - Springer Verlag
T2 - 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Y2 - 14 September 2014 through 18 September 2014
ER -