{
  "id": 240695,
  "title": "Related papers on COVID-19 Detection from chest radiographs",
  "url": "/competitions/siim-covid19-detection/discussion/240695",
  "author_name": "",
  "post_date": "2021-05-21T02:58:19.011899400Z",
  "votes": 5,
  "comment_count": 1,
  "views": 0,
  "content": "<p>This list updates related works on chest X-rays COVID-19 localization:</p>\n<ol>\n<li><a href=\"https://arxiv.org/abs/2102.06285\" target=\"_blank\">COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach</a></li>\n<li><a href=\"https://link.springer.com/article/10.1007/s42979-021-00496-w\" target=\"_blank\">Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks</a></li>\n<li><a href=\"https://arxiv.org/abs/2003.11597\" target=\"_blank\">COVID-19 Image Data Collection</a></li>\n<li><a href=\"https://www.nature.com/articles/s41746-021-00399-3\" target=\"_blank\">CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images</a></li>\n<li><a href=\"https://arxiv.org/abs/2005.12855\" target=\"_blank\">COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity</a></li>\n</ol>",
  "messages": [
    {
      "id": "1316931",
      "postDate": "05/21/2021 02:58:19",
      "content": "<p>This list updates related works on chest X-rays COVID-19 localization:</p>\n<ol>\n<li><a href=\"https://arxiv.org/abs/2102.06285\" target=\"_blank\">COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach</a></li>\n<li><a href=\"https://link.springer.com/article/10.1007/s42979-021-00496-w\" target=\"_blank\">Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks</a></li>\n<li><a href=\"https://arxiv.org/abs/2003.11597\" target=\"_blank\">COVID-19 Image Data Collection</a></li>\n<li><a href=\"https://www.nature.com/articles/s41746-021-00399-3\" target=\"_blank\">CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images</a></li>\n<li><a href=\"https://arxiv.org/abs/2005.12855\" target=\"_blank\">COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity</a></li>\n</ol>",
      "rawMarkdown": "This list updates related works on chest X-rays COVID-19 localization:\n1. [COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach](https://arxiv.org/abs/2102.06285)\n2. [Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks](https://link.springer.com/article/10.1007/s42979-021-00496-w)\n3. [COVID-19 Image Data Collection](https://arxiv.org/abs/2003.11597)\n4. [CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images](https://www.nature.com/articles/s41746-021-00399-3)\n5. [COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity](https://arxiv.org/abs/2005.12855)",
      "votes": null
    },
    {
      "id": "1322470",
      "postDate": "05/25/2021 13:19:33",
      "content": "<p>Great resource of information</p>",
      "rawMarkdown": "Great resource of information",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1322470,
      "author_name": "manojmalipeddi",
      "author_url": "",
      "post_date": "05/25/2021 13:19:33",
      "content": "<p>Great resource of information</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1316931": "This list updates related works on chest X-rays COVID-19 localization:\n1. [COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach](https://arxiv.org/abs/2102.06285)\n2. [Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks](https://link.springer.com/article/10.1007/s42979-021-00496-w)\n3. [COVID-19 Image Data Collection](https://arxiv.org/abs/2003.11597)\n4. [CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images](https://www.nature.com/articles/s41746-021-00399-3)\n5. [COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity](https://arxiv.org/abs/2005.12855)",
    "1322470": "Great resource of information"
  },
  "source": "meta"
}