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基于深度社区的大规模基于内容的 X 射线图像检索方法。

A deep community based approach for large scale content based X-ray image retrieval.

机构信息

The University of British Columbia, Vancouver, Canada.

IBM Research - Almaden Research Center, San Jose, USA.

出版信息

Med Image Anal. 2021 Feb;68:101847. doi: 10.1016/j.media.2020.101847. Epub 2020 Oct 17.

Abstract

A computer assisted system for automatic retrieval of medical images with similar image contents can serve as an efficient management tool for handling and mining large scale data, and can also be used as a tool in clinical decision support systems. In this paper, we propose a deep community based automated medical image retrieval framework for extracting similar images from a large scale X-ray database. The framework integrates a deep learning-based image feature generation approach and a network community detection technique to extract similar images. When compared with the state-of-the-art medical image retrieval techniques, the proposed approach demonstrated improved performance. We evaluated the performance of the proposed method on two large scale chest X-ray datasets, where given a query image, the proposed approach was able to extract images with similar disease labels with a precision of 85%. To the best of our knowledge, this is the first deep community based image retrieval application on large scale chest X-ray database.

摘要

一个计算机辅助的系统,可以自动检索具有相似图像内容的医学图像,可以作为处理和挖掘大规模数据的有效管理工具,也可以作为临床决策支持系统的工具。在本文中,我们提出了一个基于深度社区的自动化医学图像检索框架,用于从大规模 X 射线数据库中提取相似图像。该框架集成了基于深度学习的图像特征生成方法和网络社区检测技术,以提取相似图像。与最先进的医学图像检索技术相比,所提出的方法表现出了更好的性能。我们在两个大规模的胸部 X 射线数据集上评估了所提出方法的性能,在给定查询图像的情况下,所提出的方法能够以 85%的精度提取具有相似疾病标签的图像。据我们所知,这是第一个基于深度社区的大规模胸部 X 射线数据库图像检索应用。

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