• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Artificial intelligence applications and innovations III >Mining Patterns of Lung Infections in Chest Radiographs
【24h】

Mining Patterns of Lung Infections in Chest Radiographs

机译：胸部X线片中肺部感染的挖掘方式

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Chest radiography is a reference standard and the initial diagnostic test performed in patients who present with signs and symptoms suggesting a pulmonary infection. The most common radiographic manifestation of bacterial pulmonary infections is foci of consolidation. These are visible as bright shadows interfering with the interior lung intensities. The discovery and the assessment of bacterial infections in chest radiographs is a challenging computational task. It has been limitedly addressed as it is subject to image quality variability, content diversity, and deformability of the depicted anatomic structures. In this paper, we propose a novel approach to the discovery of consolidation patterns in chest radiographs. The proposed approach is based on non-negative matrix factorization (NMF) of statistical intensity signatures characterizing the densities of the depicted anatomic structures. Its experimental evaluation demonstrates its capability to recover semantically meaningful information from chest radiographs of patients with bacterial pulmonary infections. Moreover, the results reveal its comparative advantage over the baseline fuzzy C-means clustering approach.
机译：胸部放射线照相是参考标准，对表现出肺部感染症状和体征的患者进行初步诊断测试。细菌性肺部感染最常见的影像学表现是合并灶。这些是可见的，因为明亮的阴影会干扰内部肺部的强度。在胸部X光片中发现和评估细菌感染是一项艰巨的计算任务。由于受到图像质量可变性，内容多样性和所示解剖结构变形性的限制，因此对其进行了有限地解决。在本文中，我们提出了一种新颖的方法来发现胸部X光片中的巩固模式。所提出的方法基于统计强度特征的非负矩阵分解（NMF），该强度特征描述了所描述的解剖结构的密度。它的实验评估表明，它能够从细菌性肺部感染患者的胸片中恢复出有意义的语义信息。此外，结果显示了其相对于基线模糊C均值聚类方法的比较优势。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号