Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis

Baiying Lei, Yujia Zhao, Zhongwei Huang, Xiaoke Hao, Feng Zhou, Ahmed Elazab, Jing Qin, Haijun Lei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)


Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases are crucial as the clinical symptoms take years to appear after the onset of neuro-degeneration. This paper proposes an adaptive feature learning framework using multiple templates for early diagnosis. A multi-classification scheme is developed based on multiple brain parcellation atlases with various regions of interest. Different sets of features are extracted and then fused, and a feature selection is applied with an adaptively chosen sparse degree. In addition, both linear discriminative analysis and locally preserving projections are integrated to construct a least square regression model. Finally, we propose a feature space to predict the severity of the disease by the guidance of clinical scores. Our proposed method is validated on both Alzheimer's disease neuroimaging initiative and Parkinson's progression markers initiative databases. Extensive experimental results suggest that the proposed method outperforms the state-of-the-art methods, such as the multi-modal multi-task learning or joint sparse learning. Our method demonstrates that accurate feature learning facilitates the identification of the highly relevant brain regions with significant contribution in the prediction of disease progression. This may pave the way for further medical analysis and diagnosis in practical applications.

Original languageEnglish
Article number101632
JournalMedical Image Analysis
Publication statusPublished - Apr 2020


  • Adaptive sparse learning
  • Feature learning
  • Multi-template Multi-classification
  • Neurodegenerative disease diagnosis

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


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