MultiCost: Multi-stage cost-sensitive classification of Alzheimer's disease

Daoqiang Zhang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
Pages344-351
Number of pages8
DOIs
Publication statusPublished - 2011
Event2nd 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 - Toronto, ON, Canada
Duration: 2011 Sep 182011 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd 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
CountryCanada
CityToronto, ON
Period11/9/1811/9/18

Keywords

  • Alzheimer's disease (AD)
  • Cost-sensitive classification
  • MultiCost
  • cost-sensitive feature selection
  • multi-modality

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhang, D., & Shen, D. (2011). MultiCost: Multi-stage cost-sensitive classification of Alzheimer's disease. In Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings (pp. 344-351). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7009 LNCS). https://doi.org/10.1007/978-3-642-24319-6_42