{
  "id": "2004.12592",
  "version": "v1",
  "published": "2020-04-27T06:17:56.000Z",
  "updated": "2020-04-27T06:17:56.000Z",
  "title": "Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning",
  "authors": [
    "Tianyang Li",
    "Zhongyi Han",
    "Benzheng Wei",
    "Yuanjie Zheng",
    "Yanfei Hong",
    "Jinyu Cong"
  ],
  "comment": "Under review",
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "This paper addresses the new problem of automated screening of coronavirus disease 2019 (COVID-19) based on chest X-rays, which is urgently demanded toward fast stopping the pandemic. However, robust and accurate screening of COVID-19 from chest X-rays is still a globally recognized challenge because of two bottlenecks: 1) imaging features of COVID-19 share some similarities with other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is very high, and the misdiagnosis cost is expensive. While a few pioneering works have made much progress, they underestimate both crucial bottlenecks. In this paper, we report our solution, discriminative cost-sensitive learning (DCSL), which should be the choice if the clinical needs the assisted screening of COVID-19 from chest X-rays. DCSL combines both advantages from fine-grained classification and cost-sensitive learning. Firstly, DCSL develops a conditional center loss that learns deep discriminative representation. Secondly, DCSL establishes score-level cost-sensitive learning that can adaptively enlarge the cost of misclassifying COVID-19 examples into other classes. DCSL is so flexible that it can apply in any deep neural network. We collected a large-scale multi-class dataset comprised of 2,239 chest X-ray examples: 239 examples from confirmed COVID-19 cases, 1,000 examples with confirmed bacterial or viral pneumonia cases, and 1,000 examples of healthy people. Extensive experiments on the three-class classification show that our algorithm remarkably outperforms state-of-the-art algorithms. It achieves an accuracy of 97.01%, a precision of 97%, a sensitivity of 97.09%, and an F1-score of 96.98%. These results endow our algorithm as an efficient tool for the fast large-scale screening of COVID-19.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-04-27T06:17:56.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "chest x-ray",
      "discriminative cost-sensitive learning",
      "robust screening",
      "establishes score-level cost-sensitive learning",
      "algorithm remarkably outperforms state-of-the-art algorithms"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}