Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease

Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Transfer learning has been successfully used in the early diagnosis of Alzheimer’s disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalBrain Imaging and Behavior
DOIs
Publication statusAccepted/In press - 2018 Mar 27

Fingerprint

Early Diagnosis
Alzheimer Disease
Learning
Information Storage and Retrieval
Neuroimaging
Cerebrospinal Fluid
Magnetic Resonance Imaging
Transfer (Psychology)
Databases

Keywords

  • Alzheimer’s disease (AD)
  • Feature learning
  • Multi-label learning
  • Transfer learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Cognitive Neuroscience
  • Clinical Neurology
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health
  • Behavioral Neuroscience

Cite this

Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease. / Alzheimer’s Disease Neuroimaging Initiative.

In: Brain Imaging and Behavior, 27.03.2018, p. 1-16.

Research output: Contribution to journalArticle

@article{744b95f78e6944ceb4ab60fbeac5234e,
title = "Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease",
abstract = "Transfer learning has been successfully used in the early diagnosis of Alzheimer’s disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods.",
keywords = "Alzheimer’s disease (AD), Feature learning, Multi-label learning, Transfer learning",
author = "{Alzheimer’s Disease Neuroimaging Initiative} and Bo Cheng and Mingxia Liu and Daoqiang Zhang and Dinggang Shen",
year = "2018",
month = "3",
day = "27",
doi = "10.1007/s11682-018-9846-8",
language = "English",
pages = "1--16",
journal = "Brain Imaging and Behavior",
issn = "1931-7557",
publisher = "Springer New York",

}

TY - JOUR

T1 - Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease

AU - Alzheimer’s Disease Neuroimaging Initiative

AU - Cheng, Bo

AU - Liu, Mingxia

AU - Zhang, Daoqiang

AU - Shen, Dinggang

PY - 2018/3/27

Y1 - 2018/3/27

N2 - Transfer learning has been successfully used in the early diagnosis of Alzheimer’s disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods.

AB - Transfer learning has been successfully used in the early diagnosis of Alzheimer’s disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods.

KW - Alzheimer’s disease (AD)

KW - Feature learning

KW - Multi-label learning

KW - Transfer learning

UR - http://www.scopus.com/inward/record.url?scp=85044462330&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044462330&partnerID=8YFLogxK

U2 - 10.1007/s11682-018-9846-8

DO - 10.1007/s11682-018-9846-8

M3 - Article

SP - 1

EP - 16

JO - Brain Imaging and Behavior

JF - Brain Imaging and Behavior

SN - 1931-7557

ER -