TY - GEN
T1 - MultiCost
T2 - 2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
AU - Zhang, Daoqiang
AU - Shen, Dinggang
PY - 2011
Y1 - 2011
N2 - Most traditional classification methods for Alzheimer's disease (AD) aim to obtain a high accuracy, or equivalently a low classification error rate, which implicitly assumes that the losses of all misclassifications are the same. However, in practical AD diagnosis, the losses of misclassifying healthy subjects and AD patients are usually very different. For example, it may be troublesome if a healthy subject is misclassified as AD, but it could result in a more serious consequence if an AD patient is misclassified as healthy subject. In this paper, we propose a multi-stage cost-sensitive approach for AD classification via multimodal imaging data and CSF biomarkers. Our approach contains three key components: (1) a cost-sensitive feature selection which can select more AD-related brain regions by using different costs for different misclassifications in the feature selection stage, (2) a multimodal data fusion which effectively fuses data from MRI, PET and CSF biomarkers based on multiple kernels combination, and (3) a cost-sensitive classifier construction which further reduces the overall misclassification loss through a threshold-moving strategy. Experimental results on ADNI dataset show that the proposed approach can significantly reduce the cost of misclassification and simultaneously improve the sensitivity, under the same or even higher classification accuracy compared with conventional methods.
AB - Most traditional classification methods for Alzheimer's disease (AD) aim to obtain a high accuracy, or equivalently a low classification error rate, which implicitly assumes that the losses of all misclassifications are the same. However, in practical AD diagnosis, the losses of misclassifying healthy subjects and AD patients are usually very different. For example, it may be troublesome if a healthy subject is misclassified as AD, but it could result in a more serious consequence if an AD patient is misclassified as healthy subject. In this paper, we propose a multi-stage cost-sensitive approach for AD classification via multimodal imaging data and CSF biomarkers. Our approach contains three key components: (1) a cost-sensitive feature selection which can select more AD-related brain regions by using different costs for different misclassifications in the feature selection stage, (2) a multimodal data fusion which effectively fuses data from MRI, PET and CSF biomarkers based on multiple kernels combination, and (3) a cost-sensitive classifier construction which further reduces the overall misclassification loss through a threshold-moving strategy. Experimental results on ADNI dataset show that the proposed approach can significantly reduce the cost of misclassification and simultaneously improve the sensitivity, under the same or even higher classification accuracy compared with conventional methods.
KW - Alzheimer's disease (AD)
KW - Cost-sensitive classification
KW - MultiCost
KW - cost-sensitive feature selection
KW - multi-modality
UR - http://www.scopus.com/inward/record.url?scp=80053959264&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24319-6_42
DO - 10.1007/978-3-642-24319-6_42
M3 - Conference contribution
AN - SCOPUS:80053959264
SN - 9783642243189
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 344
EP - 351
BT - Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
Y2 - 18 September 2011 through 18 September 2011
ER -