Joint sparse and low-rank regularized multi-task multi-linear regression for prediction of infant brain development with incomplete data

Ehsan Adeli, Yu Meng, Gang Li, Weili Lin, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Studies involving dynamic infant brain development has received increasing attention in the past few years. For such studies, a complete longitudinal dataset is often required to precisely chart the early brain developmental trajectories. Whereas, in practice, we often face missing data at different time point(s) for different subjects. In this paper, we propose a new method for prediction of infant brain development scores at future time points based on longitudinal imaging measures at early time points with possible missing data. We treat this as a multi-dimensional regression problem, for predicting multiple brain development scores (multi-task) from multiple previous time points (multi-linear). To solve this problem, we propose an objective function with a joint ℓ1 and low-rank regularization on the mapping weight tensor, to enforce feature selection, while preserving the structural information from multiple dimensions. Also, based on the bag-of-words model, we propose to extract features from longitudinal imaging data. The experimental results reveal that we can effectively predict the brain development scores assessed at the age of four years, using the imaging data as early as two years of age.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
PublisherSpringer Verlag
Pages40-48
Number of pages9
Volume10433 LNCS
ISBN (Print)9783319661810
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sep 112017 Sep 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10433 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period17/9/1117/9/13

Fingerprint

Incomplete Data
Linear regression
Brain
Prediction
Imaging
Missing Data
Imaging techniques
Chart
Feature Selection
Tensors
Feature extraction
Regularization
Tensor
Objective function
Regression
Trajectories
Trajectory
Predict
Experimental Results

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Adeli, E., Meng, Y., Li, G., Lin, W., & Shen, D. (2017). Joint sparse and low-rank regularized multi-task multi-linear regression for prediction of infant brain development with incomplete data. In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (Vol. 10433 LNCS, pp. 40-48). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66182-7_5

Joint sparse and low-rank regularized multi-task multi-linear regression for prediction of infant brain development with incomplete data. / Adeli, Ehsan; Meng, Yu; Li, Gang; Lin, Weili; Shen, Dinggang.

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Vol. 10433 LNCS Springer Verlag, 2017. p. 40-48 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Adeli, E, Meng, Y, Li, G, Lin, W & Shen, D 2017, Joint sparse and low-rank regularized multi-task multi-linear regression for prediction of infant brain development with incomplete data. in Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. vol. 10433 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10433 LNCS, Springer Verlag, pp. 40-48, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 17/9/11. https://doi.org/10.1007/978-3-319-66182-7_5
Adeli E, Meng Y, Li G, Lin W, Shen D. Joint sparse and low-rank regularized multi-task multi-linear regression for prediction of infant brain development with incomplete data. In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Vol. 10433 LNCS. Springer Verlag. 2017. p. 40-48. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-66182-7_5
Adeli, Ehsan ; Meng, Yu ; Li, Gang ; Lin, Weili ; Shen, Dinggang. / Joint sparse and low-rank regularized multi-task multi-linear regression for prediction of infant brain development with incomplete data. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Vol. 10433 LNCS Springer Verlag, 2017. pp. 40-48 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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