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[会议论文]

MCI Conversion Prediction Based on Transfer Learning

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作者:

Lin, Lan (Lin, Lan.) | Zhang, Bai-wen (Zhang, Bai-wen.)

收录:

CPCI-S

摘要:

Amnestic mild cognitive impairment (MCI) commonly represents an intermediate stage situated in the spectrum between normal age-related cognitive decline and dementia. Predicting of MCI conversion to Alzheimer's Disease (AD) plays critical roles in early diagnosis and disease-modifying therapies. We analyzed baseline 3T MRI scans in 337 MCI patients from the ADNI-GO and ANDI-2 cohorts. The subjects were divided into MCI non-converters (MCInc) and MCI converters (MCIc). To evaluate conversion rates, we aim to first extract intermediate representations of structural MRI (sMRI) by a pre-trained convolutional neural network (CNN) model, then combine principal component analysis (PCA) and sequential feature selection (SFS) for feature selection, and finally adopt support vector machine (SVM) for prediction. The method attained an accuracy of 77.58%, a sensitivity of 90.48%, a specificity of 76.42%, which may be useful and practical for clinical diagnosis.

关键词:

Transfer learning Alzheimer's disease (AD) Deep learning Mild cognitive impairment (MCI)

作者机构:

  • [ 1 ] [Lin, Lan]Beijing Univ Technol, Alzheimers Dis Neuroimaging Initiat ADNI, Beijing, Peoples R China
  • [ 2 ] [Zhang, Bai-wen]Beijing Univ Technol, Alzheimers Dis Neuroimaging Initiat ADNI, Beijing, Peoples R China
  • [ 3 ] [Lin, Lan]Beijing Univ Technol, Coll Life Sci & Bioengn, Beijing, Peoples R China
  • [ 4 ] [Zhang, Bai-wen]Beijing Univ Technol, Coll Life Sci & Bioengn, Beijing, Peoples R China

通讯作者信息:

  • [Lin, Lan]Beijing Univ Technol, Alzheimers Dis Neuroimaging Initiat ADNI, Beijing, Peoples R China;;[Zhang, Bai-wen]Beijing Univ Technol, Alzheimers Dis Neuroimaging Initiat ADNI, Beijing, Peoples R China;;[Lin, Lan]Beijing Univ Technol, Coll Life Sci & Bioengn, Beijing, Peoples R China;;[Zhang, Bai-wen]Beijing Univ Technol, Coll Life Sci & Bioengn, Beijing, Peoples R China

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来源 :

2018 INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION AND NETWORK TECHNOLOGY (CCNT 2018)

ISSN: 2475-8841

年份: 2018

卷: 291

页码: 218-222

语种: 英文

被引次数:

WoS核心集被引频次: 1

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