登录
 机构网站
  1. 首页
  2. 情报产品
  3. 快讯详情

《MedRxiv，3月17日，Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label》

  • 来源专题：COVID-19科研动态监测
  • 编译者： zhangmin
  • 发布时间：2020-03-18

  • Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label

    Chuansheng Zheng, Xianbo Deng, Qing Fu, Qiang Zhou, Jiapei Feng, Hui Ma, Wenyu Liu, Xinggang Wang

    doi: https://doi.org/10.1101/2020.03.12.20027185

    Abstract

    Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 detection on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning-based software system was developed using 3D CT volumes to detect COVID-19. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious.

    *注，本文为预印本论文手稿，是未经同行评审的初步报告，其观点仅供科研同行交流，并不是结论性内容，请使用者谨慎使用.

  • 原文来源：https://www.medrxiv.org/content/10.1101/2020.03.12.20027185v1
141浏览量
原文链接
相关报告

  • 《MedRxiv，2月17日，A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)》
    • 来源专题：COVID-19科研动态监测
    • 编译者：dingxq
    • 发布时间：2020-02-18
    • A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) Shuai Wang, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, Mengjiao Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng, Bo Xu doi: https://doi.org/10.1101/2020.02.14.20023028 Abstract The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused approximately 64,000 cases of Corona Virus Disease (COVID-19) in China so far, with that number continuing to grow. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Viral nucleic acid testing based on specimens from the lower respiratory tract is the diagnostic gold standard. However, the availability and quality of laboratory testing in the infected region presents a challenge, so alternative diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we hypothesized that Artificial Intelligence's deep learning methods might be able to extract COVID-19's specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. To test this possibility, we collected 453 CT images of pathogen-confirmed COVID-19 cases along with previously diagnosed with typical viral pneumonia. 217 images were used as the training set and the inception migration-learning model was used to establish the algorithm. The internal validation achieved a total accuracy of 82.9% with specificity of 80.5% and sensitivity of 84%. The external testing dataset showed a total accuracy of 73.1% with specificity of 67% and sensitivity of 74%. These results indicate the great value of using the deep learning method to extract radiological graphical features for COVID-19 diagnosis. *注，本文为预印本论文手稿，是未经同行评审的初步报告，其观点仅供科研同行交流，并不是结论性内容，请使用者谨慎使用.
    100浏览量
    原文链接

  • 《MedRxiv，2月18日，A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)》
    • 来源专题：COVID-19科研动态监测
    • 编译者：xuwenwhlib
    • 发布时间：2020-02-19
    • A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) Shuai Wang, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, Mengjiao Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng, Bo Xu doi: https://doi.org/10.1101/2020.02.14.20023028 Abstract Background: To control the spread of Corona Virus Disease (COVID-19), screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the diagnostic gold standard but it is time consuming with significant false positive results. Fast and accurate diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we hypothesized that deep learning methods might be able to extract COVID-19's graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods：We collected 453 CT images of pathogen-confirmed COVID-19 cases along with previously diagnosed with typical viral pneumonia. We modified the Inception migration-learning model to establish the algorithm, followed by internal and external validation. Findings: The internal validation achieved a total accuracy of 82.9% with specificity of 80.5% and sensitivity of 84%. The external testing dataset showed a total accuracy of 73.1% with specificity of 67% and sensitivity of 74%. Interpretation: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Funding: No funding is involved in the execution of the project. *注，本文为预印本论文手稿，是未经同行评审的初步报告，其观点仅供科研同行交流，并不是结论性内容，请使用者谨慎使用.
    92浏览量
    原文链接