• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>SPIE Medical Imaging Conference >Localizing tuberculosis in chest radiographs with deep learning
【24h】

Localizing tuberculosis in chest radiographs with deep learning

机译：利用深度学习的胸部射线照相本地化结核病

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Chest radiography (CXR) has been used as an effective tool for screening tuberculosis (TB). Because of the lack of radiological expertise in resource-constrained regions, automatic analysis of CXR is appealing as a "first reader'. In addition to screening the CXR for disease, it is critical to highlight locations of the disease in abnormal CXRs. In this paper, we focus on the task of locating TB in CXRs which is more challenging due to the intrinsic difficulty of locating the abnormality. The method is based on applying a convolutional neural network (CNN) to classify the superpixels generated from the lung area. Specifically, it consists of four major components: lung ROI extraction, superpixel segmentation, multi-scale patch generation/labeling, and patch classification. The TB regions are located by identifying those superpixels whose corresponding patches are classified as abnormal by the CNN. The method is tested on a publicly available TB CXR dataset which contains 336 TB images showing various manifestations of TB. The TB regions in the images were marked by radiologists. To evaluate the method, the images are split into training, validation, and test sets with all the manifestations being represented in each set. The performance is evaluated at both the patch level and image level. The classification accuracy on the patch test set is 72.8% and the average Dice index for the test images is 0.67. The factors that may contribute to misclassification are discussed and directions for future work are addressed.
机译：胸部射线照相（CXR）已被用作筛选结核病（TB）的有效工具。由于资源受限区域缺乏放射性专业知识，CXR的自动分析是“第一读者”的吸引力。除了筛查CXR疾病外，对于突出CXR异常突出疾病的位置至关重要。在此纸张，我们专注于在CXR中定位结核病的任务，这是由于定位异常的内在难度而更具挑战性的。该方法基于应用卷积神经网络（CNN）来分类从肺区产生的超像素。特别它由四个主要成分组成：肺投资回报率提取，超顶旋装分割，多尺度补丁生成/标签和补丁分类。通过识别其对应贴片被CNN分类为异常的超像素来定位TB区域。该方法是在公开的TB CXR数据集上进行测试，其中包含336个TB图像，显示TB的各种表现形式。图像中的TB区域由放射线癖标记TS。为了评估该方法，将图像分成训练，验证和测试集，其中每个集合中都表示的所有表现形式。在补丁级别和图像级别评估性能。贴片测试集的分类精度为72.8％，测试图像的平均骰子索引为0.67。讨论可能有助于错误分类的因素，并解决未来工作的指示。

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