Deep learning for cerebellar ataxia classification and functional score regression

Zhen Yang, Shenghua Zhong, Aaron Carass, Sarah H. Ying, Jerry Ladd Prince

Research output: Contribution to journalArticle

Abstract

Cerebellar ataxia is a progressive neuro-degenerative disease that has multiple genetic versions, each with a characteristic pattern of anatomical degeneration that yields distinctive motor and cognitive problems. Studying this pattern of degeneration can help with the diagnosis of disease subtypes, evaluation of disease stage, and treatment planning. In this work, we propose a learning framework using MR image data for discriminating a set of cerebellar ataxia types and predicting a disease related functional score. We address the difficulty in analyzing high-dimensional image data with limited training subjects by: 1) training weak classifiers/regressors on a set of image subdomains separately, and combining the weak classifier/regressor outputs to make the decision; 2) perturbing the image subdomain to increase the training samples; 3) using a deep learning technique called the stacked auto-encoder to develop highly representative feature vectors of the input data. Experiments show that our approach can reliably classify between one of four categories (healthy control and three types of ataxia), and predict the functional staging score for ataxia.

Original languageEnglish (US)
Pages (from-to)68-76
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8679
StatePublished - 2014

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Regression
Classifiers
Degeneration
Neurodegenerative diseases
Classifier
Training Samples
Encoder
Feature Vector
Planning
High-dimensional
Classify
Predict
Learning
Deep learning
Output
Evaluation
Experiments
Experiment
Training

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

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