计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 367-369.doi: 10.11896/jsjkx.201200152

• 图像处理& 多媒体技术 • 上一篇    下一篇

基于弱监督的深度学习胸部X光疾病诊断与定位方法

康明   

  1. 中国社会科学院研究生院 北京102488
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 康明(2753880966@qq.com)

Method for Diagnosis and Location of Chest X-ray Diseases with Deep Learning Based on Weak Supervision

KANG Ming   

  1. Graduate School of Chinese Academy of Social Sciences,Beijing 102488,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:KANG Ming,born in 1979,Ph.D,is a member of China Computer Federation.His main research interests include deep learning and artificial intelligence.

摘要: 胸部疾病是最常见的疾病之一,胸部X光是检查和诊断胸部疾病的重要方法。构建一个高度准确的胸部诊断预测模型通常需要大量的标签和异常位置的手工标注。然而,获取这种带标注的数据是很困难的,特别是带有位置标注的数据。因此,构建一个仅只使用少量位置标注就能正常工作的方法成为当前面临的主要问题。虽然目前已经有一些弱监督方法来解决这个问题,但是它们大多只关注于图像信息,很少考虑到图像间的关系信息。因此,提出一种基于弱监督深度学习的胸部X光疾病诊断与定位模型,在运用深度学习方法提取图像信息的同时,引入了图结构,运用哈希编码衡量图像本身的相似度,并将图像的相关信息也加入到学习过程中,在不需要额外标注的情况下,能够通过少量的标注达到很好的识别和定位效果。经过在Chest X-ray数据集上验证,在仅使用3%带位置标签的数据的情况下,定位准确率(IoU)达到44%。这表明,该方法能够有效识别和定位胸部X光病灶,为医生提供用于筛查的候选区域,辅助医生进行胸部疾病的诊断。

关键词: 疾病诊断与定位, 卷积神经网络, 弱监督, 深度学习, 胸部X光

Abstract: Chest disease is one of the most common diseases,chest X-ray is an important method for examination and diagnosis of chest disease.The construction of a highly accurate prediction model for chest diagnosis usually requires a large number of tags and manual annotation of abnormal locations.However,it is difficult to obtain such annotated data,especially data with location annotation.Therefore,building a method that use only a few location annotations becomes a major problem.Although there have been some weak supervision methods to solve this problem,most of them only focus on the image information,rarely considering the relationship between the images.Therefore,a chest X-ray disease diagnosis and location model based on weak supervised deep learning is proposed.While deep learning is used to extract the image information,the graph structure is introduced and hash code is used to add the similarity of the image itself and the relational information of the image into the learning process.In the case of no additional annotation,a small amount of annotation can achieve a good recognition and location effect.After validation on the Chest X-ray dataset,the location accuracy (IoU) is 44% when using only 3% of the location-tagged data.This indicates that this method can effectively identify and locate chest X-ray lesions,provide doctors with candidate areas for screening,and assist doctors in the diagnosis of chest diseases.

Key words: Chest X-ray, Convolutional neural network, Deep learning, Disease diagnosis and localization, Weak supervision

中图分类号: 

  • TP183
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