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author:

Tong, Tong (Tong, Tong.) [1] | Gray, Katherine (Gray, Katherine.) [2] | Gao, Qinquan (Gao, Qinquan.) [3] (Scholars:高钦泉) | Chen, Liang (Chen, Liang.) [4] | Rueckert, Daniel (Rueckert, Daniel.) [5]

Indexed by:

EI Scopus SCIE

Abstract:

Accurate diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI) is of great interest to patients and clinicians. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis and prognosis. Classification methods are needed to combine these multiple biomarkers to provide an accurate diagnosis. State-of-the-art approaches calculate a mixed kernel or a similarity matrix by linearly combining kernels or similarities from multiple modalities. However, the complementary information from multi-modal data are not necessarily linearly related. In addition, this linear combination is also sensitive to the weights assigned to each modality. In this paper, we present a multi-modality classification framework to efficiently exploit the complementarity in the multi-modal data. First, pairwise similarity is calculated for each modality individually using the features including regional MRI volumes, voxel-based FDG-PET signal intensities, CSF biomarker measures, and categorical genetic information. Similarities from multiple modalities are then combined in a nonlinear graph fusion process, which generates a unified graph for final classification. Based on the unified graphs, we achieved classification area under curve (AUC) of receiver-operator characteristic of 98.1% between AD subjects and normal controls (NC), 82.4% between MCI subjects and NC and 77.9% in a three-way classification, which are significantly better than those using single-modality biomarkers and those based on state-of-the-art linear combination approaches.

Keyword:

Biomarkers Classification of Alzheimer's disease Machine learning Multiple modalities Nonlinear graph fusion

Community:

  • [ 1 ] [Tong, Tong]Imperial Coll London, Dept Comp, Biomed Image Anal Grp, 180 Queens Gate, London SW7 2AZ, England
  • [ 2 ] [Gray, Katherine]Imperial Coll London, Dept Comp, Biomed Image Anal Grp, 180 Queens Gate, London SW7 2AZ, England
  • [ 3 ] [Chen, Liang]Imperial Coll London, Dept Comp, Biomed Image Anal Grp, 180 Queens Gate, London SW7 2AZ, England
  • [ 4 ] [Rueckert, Daniel]Imperial Coll London, Dept Comp, Biomed Image Anal Grp, 180 Queens Gate, London SW7 2AZ, England
  • [ 5 ] [Gao, Qinquan]Fuzhou Univ, Fujian Prov Key Lab Med Instrument & Pharmaceut T, Fuzhou, Peoples R China
  • [ 6 ] [Tong, Tong]MGH Harvard Med Sch, Athinoula A Martins Ctr Biomed Imaging, Lab Computat Neuroimaging, Charlestown, MA USA

Reprint 's Address:

  • 高钦泉

    [Gao, Qinquan]Fuzou Univ, Dept Internet Things, Fujian Prov Key Lab Med Instrument & Pharmaceut T, Fuzhou, Peoples R China

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Source :

PATTERN RECOGNITION

ISSN: 0031-3203

Year: 2017

Volume: 63

Page: 171-181

3 . 9 6 2

JCR@2017

7 . 5 0 0

JCR@2023

ESI Discipline: ENGINEERING;

ESI HC Threshold:177

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count: 158

SCOPUS Cited Count: 185

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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