A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images
- University of Turin（都灵大学）
- Azienda Ospedaliera Città della Salute e della Scienza Presidio Molinette（健康与科学城医院莫利内特分院）
- AOU San Luigi Gonzaga（圣路易吉·贡扎加大学医院）
机构由 AI 辅助整理，请以论文原文为准。
英文摘要：
Early screening of patients is a critical issue in order to assess immediate and fast responses against the spread of COVID-19. The use of nasopharyngeal swabs has been considered the most viable approach; however, the result is not immediate or, in the case of fast exams, sufficiently accurate. Using Chest X-Ray (CXR) imaging for early screening potentially provides faster and more accurate response; however, diagnosing COVID from CXRs is hard and we should rely on deep learning support, whose decision process is, on the other hand, "black-boxed" and, for such reason, untrustworthy. We propose an explainable two-step diagnostic approach, where we first detect known pathologies (anomalies) in the lungs, on top of which we diagnose the illness. Our approach achieves promising performance in COVID detection, compatible with expert human radiologists. All of our experiments have been carried out bearing in mind that, especially for clinical applications, explainability plays a major role for building trust in machine learning algorithms.
