Generative-discriminative basis learning for medical imaging

Nematollah K. Batmanghelich, Ben Taskar, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


This paper presents a novel dimensionality reduction method for classification in medical imaging. The goal is to transform very high-dimensional input (typically, millions of voxels) to a low-dimensional representation (small number of constructed features) that preserves discriminative signal and is clinically interpretable. We formulate the task as a constrained optimization problem that combines generative and discriminative objectives and show how to extend it to the semi-supervised learning (SSL) setting. We propose a novel large-scale algorithm to solve the resulting optimization problem. In the fully supervised case, we demonstrate accuracy rates that are better than or comparable to state-of-the-art algorithms on several datasets while producing a representation of the group difference that is consistent with prior clinical reports. Effectiveness of the proposed algorithm for SSL is evaluated with both benchmark and medical imaging datasets. In the benchmark datasets, the results are better than or comparable to the state-of-the-art methods for SSL. For evaluation of the SSL setting in medical datasets, we use images of subjects with mild cognitive impairment (MCI), which is believed to be a precursor to Alzheimer's disease (AD), as unlabeled data. AD subjects and normal control (NC) subjects are used as labeled data, and we try to predict conversion from MCI to AD on follow-up. The semi-supervised extension of this method not only improves the generalization accuracy for the labeled data (AD/NC) slightly but is also able to predict subjects which are likely to converge to AD.

Original languageEnglish (US)
Article number5961630
Pages (from-to)51-69
Number of pages19
JournalIEEE transactions on medical imaging
Issue number1
StatePublished - Jan 2012
Externally publishedYes


  • Basis learning
  • classification
  • feature construction
  • generative-discriminative learning
  • machine learning
  • matrix factorization
  • morphological pattern analysis
  • optimization
  • semi-supervised learning
  • sparsity

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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