Fast Neuroimaging-Based Retrieval for Alzheimer's Disease Analysis.

Xiaofeng Zhu, Kim-Han Thung, Jun Zhang, Dinggang She
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Abstract

This paper proposes a framework of fast neuroimaging-based retrieval and AD analysis, by three key steps: (1) landmark detection, which efficiently extracts landmark-based neuroimaging features without the need of nonlinear registration in testing stage; (2) landmark selection, which removes redundant/noisy landmarks via proposing a feature selection method that considers structural information among landmarks; and (3) hashing, which converts high-dimensional features of subjects into binary codes, for efficiently conducting approximate nearest neighbor search and diagnosis of AD. We have conducted experiments on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and demonstrated that our framework could achieve higher performance than the comparison methods, in terms of accuracy and speed (at least 100 times faster).

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基于神经影像的阿尔茨海默病快速检索分析。
本文通过三个关键步骤提出了基于神经影像的快速检索和 AD 分析框架:(1) 地标检测:无需在测试阶段进行非线性配准,即可高效提取基于地标的神经影像特征;(2) 地标选择:通过提出一种考虑地标间结构信息的特征选择方法,去除冗余/噪声地标;(3) 散列:将受试者的高维特征转换为二进制代码,以高效进行近似近邻搜索和 AD 诊断。我们在阿尔茨海默病神经影像计划(ADNI)数据集上进行了实验,结果表明,我们的框架在准确性和速度方面(至少快 100 倍)都能达到比对比方法更高的性能。
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