小脑共济失调分类与功能评分回归的深度学习。

Zhen Yang, Shenghua Zhong, Aaron Carass, Sarah H Ying, Jerry L Prince
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引用次数: 25

摘要

小脑性共济失调是一种进行性神经退行性疾病,具有多种遗传版本,每种版本都具有解剖变性的特征模式,从而产生独特的运动和认知问题。研究这种退化模式有助于疾病亚型的诊断、疾病分期的评估和治疗计划。在这项工作中,我们提出了一个使用MR图像数据的学习框架,用于区分一组小脑共济失调类型并预测疾病相关的功能评分。针对训练对象有限的高维图像数据分析困难的问题:1)在一组图像子域上分别训练弱分类器/回归器,并结合弱分类器/回归器输出进行决策;2)扰动图像子域,增加训练样本;3)使用一种称为堆叠自编码器的深度学习技术来开发输入数据的高度代表性特征向量。实验表明,我们的方法可以可靠地在四种类型(健康对照和三种类型的共济失调)之间进行分类,并预测共济失调的功能分期评分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression.

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.

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