Tree-guided sparse coding for brain disease classification.

Manhua Liu, Daoqiang Zhang, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

15 Citations (Scopus)

Abstract

Neuroimage analysis based on machine learning technologies has been widely employed to assist the diagnosis of brain diseases such as Alzheimer's disease and its prodromal stage--mild cognitive impairment. One of the major problems in brain image analysis involves learning the most relevant features from a huge set of raw imaging features, which are far more numerous than the training samples. This makes the tasks of both disease classification and interpretation extremely challenging. Sparse coding via L1 regularization, such as Lasso, can provide an effective way to select the most relevant features for alleviating the curse of dimensionality and achieving more accurate classification. However, the selected features may distribute randomly throughout the whole brain, although in reality disease-induced abnormal changes often happen in a few contiguous regions. To address this issue, we investigate a tree-guided sparse coding method to identify grouped imaging features in the brain regions for guiding disease classification and interpretation. Spatial relationships of the image structures are imposed during sparse coding with a tree-guided regularization. Our experimental results on the ADNI dataset show that the tree-guided sparse coding method not only achieves better classification accuracy, but also allows for more meaningful diagnosis of brain diseases compared with the conventional L1-regularized LASSO.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages239-247
Number of pages9
Volume15
EditionPt 3
Publication statusPublished - 2012 Dec 1

Fingerprint

Brain Diseases
Brain
Prodromal Symptoms
Alzheimer Disease
Learning
Technology

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Liu, M., Zhang, D., Yap, P. T., & Shen, D. (2012). Tree-guided sparse coding for brain disease classification. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 15, pp. 239-247)

Tree-guided sparse coding for brain disease classification. / Liu, Manhua; Zhang, Daoqiang; Yap, Pew Thian; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 15 Pt 3. ed. 2012. p. 239-247.

Research output: Chapter in Book/Report/Conference proceedingChapter

Liu, M, Zhang, D, Yap, PT & Shen, D 2012, Tree-guided sparse coding for brain disease classification. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 edn, vol. 15, pp. 239-247.
Liu M, Zhang D, Yap PT, Shen D. Tree-guided sparse coding for brain disease classification. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 ed. Vol. 15. 2012. p. 239-247
Liu, Manhua ; Zhang, Daoqiang ; Yap, Pew Thian ; Shen, Dinggang. / Tree-guided sparse coding for brain disease classification. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 15 Pt 3. ed. 2012. pp. 239-247
@inbook{dfe102761fb9499fbbea2681a63af480,
title = "Tree-guided sparse coding for brain disease classification.",
abstract = "Neuroimage analysis based on machine learning technologies has been widely employed to assist the diagnosis of brain diseases such as Alzheimer's disease and its prodromal stage--mild cognitive impairment. One of the major problems in brain image analysis involves learning the most relevant features from a huge set of raw imaging features, which are far more numerous than the training samples. This makes the tasks of both disease classification and interpretation extremely challenging. Sparse coding via L1 regularization, such as Lasso, can provide an effective way to select the most relevant features for alleviating the curse of dimensionality and achieving more accurate classification. However, the selected features may distribute randomly throughout the whole brain, although in reality disease-induced abnormal changes often happen in a few contiguous regions. To address this issue, we investigate a tree-guided sparse coding method to identify grouped imaging features in the brain regions for guiding disease classification and interpretation. Spatial relationships of the image structures are imposed during sparse coding with a tree-guided regularization. Our experimental results on the ADNI dataset show that the tree-guided sparse coding method not only achieves better classification accuracy, but also allows for more meaningful diagnosis of brain diseases compared with the conventional L1-regularized LASSO.",
author = "Manhua Liu and Daoqiang Zhang and Yap, {Pew Thian} and Dinggang Shen",
year = "2012",
month = "12",
day = "1",
language = "English",
volume = "15",
pages = "239--247",
booktitle = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",
edition = "Pt 3",

}

TY - CHAP

T1 - Tree-guided sparse coding for brain disease classification.

AU - Liu, Manhua

AU - Zhang, Daoqiang

AU - Yap, Pew Thian

AU - Shen, Dinggang

PY - 2012/12/1

Y1 - 2012/12/1

N2 - Neuroimage analysis based on machine learning technologies has been widely employed to assist the diagnosis of brain diseases such as Alzheimer's disease and its prodromal stage--mild cognitive impairment. One of the major problems in brain image analysis involves learning the most relevant features from a huge set of raw imaging features, which are far more numerous than the training samples. This makes the tasks of both disease classification and interpretation extremely challenging. Sparse coding via L1 regularization, such as Lasso, can provide an effective way to select the most relevant features for alleviating the curse of dimensionality and achieving more accurate classification. However, the selected features may distribute randomly throughout the whole brain, although in reality disease-induced abnormal changes often happen in a few contiguous regions. To address this issue, we investigate a tree-guided sparse coding method to identify grouped imaging features in the brain regions for guiding disease classification and interpretation. Spatial relationships of the image structures are imposed during sparse coding with a tree-guided regularization. Our experimental results on the ADNI dataset show that the tree-guided sparse coding method not only achieves better classification accuracy, but also allows for more meaningful diagnosis of brain diseases compared with the conventional L1-regularized LASSO.

AB - Neuroimage analysis based on machine learning technologies has been widely employed to assist the diagnosis of brain diseases such as Alzheimer's disease and its prodromal stage--mild cognitive impairment. One of the major problems in brain image analysis involves learning the most relevant features from a huge set of raw imaging features, which are far more numerous than the training samples. This makes the tasks of both disease classification and interpretation extremely challenging. Sparse coding via L1 regularization, such as Lasso, can provide an effective way to select the most relevant features for alleviating the curse of dimensionality and achieving more accurate classification. However, the selected features may distribute randomly throughout the whole brain, although in reality disease-induced abnormal changes often happen in a few contiguous regions. To address this issue, we investigate a tree-guided sparse coding method to identify grouped imaging features in the brain regions for guiding disease classification and interpretation. Spatial relationships of the image structures are imposed during sparse coding with a tree-guided regularization. Our experimental results on the ADNI dataset show that the tree-guided sparse coding method not only achieves better classification accuracy, but also allows for more meaningful diagnosis of brain diseases compared with the conventional L1-regularized LASSO.

UR - http://www.scopus.com/inward/record.url?scp=84872896394&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872896394&partnerID=8YFLogxK

M3 - Chapter

VL - 15

SP - 239

EP - 247

BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

ER -