Domain transfer learning for MCI conversion prediction

Authors:
Bo Cheng;Daoqiang Zhang;Dinggang Shen
Affiliations:
Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China, Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC;Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China, Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC;Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

Citing 3
Cited 0

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

The Journal of Machine Learning Research
Cross-domain video concept detection using adaptive svms

Proceedings of the 15th international conference on Multimedia
Increasing power to predict mild cognitive impairment conversion to alzheimer's disease using hippocampal atrophy rate and statistical shape models

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent studies of Alzheimer's disease (AD), it has increasing attentions in identifying mild cognitive impairment (MCI) converters (MCI-C) from MCI non-converters (MCI-NC). Note that MCI is a prodromal stage of AD, with possibility to convert to AD. Most traditional methods for MCI conversion prediction learn information only from MCI subjects (including MCI-C and MCI-NC), not from other related subjects, e.g., AD and normal controls (NC), which can actually aid the classification between MCI-C and MCI-NC. In this paper, we propose a novel domain-transfer learning method for MCI conversion prediction. Different from most existing methods, we classify MCI-C and MCI-NC with aid from the domain knowledge learned with AD and NC subjects as auxiliary domain to further improve the classification performance. Our method contains two key components: (1) the cross-domain kernel learning for transferring auxiliary domain knowledge, and (2) the adapted support vector machine (SVM) decision function construction for cross-domain and auxiliary domain knowledge fusion. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that the proposed method can significantly improve the classification performance between MCI-C and MCI-NC, with aid of domain knowledge learned from AD and NC subjects.