论文标题
从胸部X射线进行深度学习：一场小数据
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
论文作者
论文摘要
使用广泛而简单的胸部X射线（CXR）成像进行早期筛查Covid-19患者的可能性引起了临床和AI社区的极大兴趣。在这项研究中，我们提供了见解，并通过将深度学习应用于CXR图像的共同分类来提出有关合理期望的警告。我们提供了一套方法论指南和批判性阅读，以使用当前可用的数据集获得一系列广泛的统计结果。特别是，我们采取了当前的小型互联数据所带来的挑战，并显示了使用较大的公共非旋转CXR数据集转移学习引起的偏见。我们还通过为中等大小的COVID CXR数据集提供结果来做出贡献，该数据集刚刚由意大利北部的一家急诊医院之一收集。这些新的数据使我们能够促进科学界循环的初步结果的概括能力。我们的结论阐明了使用CXR有效区分共同的可能性。
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.
