论文标题
LOC-VAE：从3D脑MR图像中学习结构局部的表示，以基于内容的图像检索
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval
论文作者
论文摘要
基于内容的图像检索（CBIR）系统是一种支持阅读和解释医学图像的新兴技术。由于3D脑MR图像是高维度的，因此使用机器学习技术CBIR是必需的。另外，对于可靠的CBIR系统，所得的低维表示中的每个维度都必须与神经学解释的区域相关联。我们提出了一个局部变异自动编码器（LOC-VAE），该变量自动编码器（LOC-VAE）提供了来自3D脑MR图像的神经解释性可解释的低维度表示，用于临床CBIR。 LOC-VAE基于$β$ -VAE的附加约束，即低维表示的每个维度都与大脑的局部区域相对应。所提出的LOC-VAE能够获取保留疾病特征的代表，即使在高维压缩比下也是高度局部的（4096：1）。与天真的$β$ -VAE相比，通过LOC-VAE获得的低维表示将每个维度的位置度量提高了4.61点，同时保持了可比的脑重建能力和有关阿尔茨海默氏病诊断的信息。
Content-based image retrieval (CBIR) systems are an emerging technology that supports reading and interpreting medical images. Since 3D brain MR images are high dimensional, dimensionality reduction is necessary for CBIR using machine learning techniques. In addition, for a reliable CBIR system, each dimension in the resulting low-dimensional representation must be associated with a neurologically interpretable region. We propose a localized variational autoencoder (Loc-VAE) that provides neuroanatomically interpretable low-dimensional representation from 3D brain MR images for clinical CBIR. Loc-VAE is based on $β$-VAE with the additional constraint that each dimension of the low-dimensional representation corresponds to a local region of the brain. The proposed Loc-VAE is capable of acquiring representation that preserves disease features and is highly localized, even under high-dimensional compression ratios (4096:1). The low-dimensional representation obtained by Loc-VAE improved the locality measure of each dimension by 4.61 points compared to naive $β$-VAE, while maintaining comparable brain reconstruction capability and information about the diagnosis of Alzheimer's disease.
