Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, 19107, USA.
Sci Rep. 2021 Aug 9;11(1):16075. doi: 10.1038/s41598-021-95561-y.
The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.
新型冠状病毒于 2020 年初引发了全球大流行，其死亡率是流感的数倍。随着感染人数的飙升，检测能力却远远落后，胸部 X 光（CXR）成像在疑似或确诊 COVID-19 感染患者的早期诊断和治疗计划中变得更加重要。在短短几周内，使用深度学习提出的新肺部筛查方法迅速出现，而质量保证讨论却滞后了。本文提出了一套验证深度学习算法的协议，包括我们的 ROI 捉迷藏协议，该协议强调或隐藏 CXR 数据中的关键感兴趣区域。我们的协议允许评估异常检测的分类性能及其与放射学特征的相关性，这是迄今为止提出的几种深度学习方法中被忽视的重要问题。通过在公共图像数据集上使用 CXR 表示运行一组系统测试，我们展示了当前技术的弱点，并提供了在使用异构数据源时自动化放射学分析的优势和局限性的观点。
