Generative-discriminative basis learning for medical imaging.
Generative-discriminative basis learning for medical imaging.
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DOI：
10.1109/tmi.2011.2162961
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发表时间：
2012-01
影响因子：
10.6
通讯作者：
Davatzikos C
中科院分区：
文献类型：
--
作者：
Batmanghelich NK;Taskar B;Davatzikos C
本文提出了一种新的降维方法，用于医学图像分类。其目标是将非常高维的输入（通常是数百万的体素）转换为低维表示（少量的构造特征），该表示保留了区分信号并且是临床上可解释的。我们制定的任务作为一个约束优化问题，结合生成和歧视的目标，并展示如何将其扩展到半监督学习（SSL）设置。我们提出了一种新的大规模算法来解决由此产生的优化问题。在完全监督的情况下，我们在几个数据集上证明了比最先进算法更好或相当的准确率，同时产生了与先前临床报告一致的组差异表示。所提出的算法SSL的有效性进行评估与基准和医学成像数据集。在基准数据集中，结果优于或相当于SSL的最先进的方法。为了评估医疗数据集中的SSL设置，我们使用轻度认知障碍（MCI）受试者的图像作为未标记数据，MCI被认为是阿尔茨海默病（AD）的前兆。AD受试者和正常对照（NC）受试者被用作标记数据，我们试图预测随访时从MCI向AD的转化。该方法的半监督扩展不仅略微提高了标记数据（AD/NC）的泛化精度，而且还能够预测可能收敛到AD的主题。
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.