Deep ensemble learning of sparse regression models for brain disease diagnosis

Heung Il Suk, Seong Whan Lee, Dinggang Shen

Research output: Research - peer-reviewArticle

  • 6 Citations

Abstract

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.

LanguageEnglish (US)
Pages101-113
Number of pages13
JournalMedical Image Analysis
Volume37
DOIs
StatePublished - Apr 1 2017

Fingerprint

Brain Diseases
Learning
Brain
Deep learning
Machine Learning
Learning systems
Neuroimaging
Alzheimer Disease
Cognitive Dysfunction
Clinical Decision-Making
Medical problems
Labels
Decision making
Neural networks
Imaging techniques
Experiments
Deep neural networks

Keywords

  • Alzheimer's disease
  • Convolutional neural network
  • Deep ensemble learning
  • Sparse regression model

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

Cite this

Deep ensemble learning of sparse regression models for brain disease diagnosis. / Suk, Heung Il; Lee, Seong Whan; Shen, Dinggang.

In: Medical Image Analysis, Vol. 37, 01.04.2017, p. 101-113.

Research output: Research - peer-reviewArticle

@article{d6684f4958254f928b128364c4f66068,
title = "Deep ensemble learning of sparse regression models for brain disease diagnosis",
abstract = "Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.",
keywords = "Alzheimer's disease, Convolutional neural network, Deep ensemble learning, Sparse regression model",
author = "Suk, {Heung Il} and Lee, {Seong Whan} and Dinggang Shen",
year = "2017",
month = "4",
doi = "10.1016/j.media.2017.01.008",
volume = "37",
pages = "101--113",
journal = "Medical Image Analysis",
issn = "1361-8415",
publisher = "Elsevier",

}

TY - JOUR

T1 - Deep ensemble learning of sparse regression models for brain disease diagnosis

AU - Suk,Heung Il

AU - Lee,Seong Whan

AU - Shen,Dinggang

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.

AB - Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.

KW - Alzheimer's disease

KW - Convolutional neural network

KW - Deep ensemble learning

KW - Sparse regression model

UR - http://www.scopus.com/inward/record.url?scp=85012254441&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85012254441&partnerID=8YFLogxK

U2 - 10.1016/j.media.2017.01.008

DO - 10.1016/j.media.2017.01.008

M3 - Article

VL - 37

SP - 101

EP - 113

JO - Medical Image Analysis

T2 - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

ER -