Quick and accurate diagnosis is of paramount importance to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we investigate the impact of multi-task learning (classification and segmentation) on the ability of CNNs to differentiate between various appearances of COVID-19 infections in the lung. We also employ self-supervised pre-training approaches, namely MoCo and inpainting-CXR, to eliminate the dependence on expensive ground truth annotations for COVID-19 classification. Finally, we conduct a critical evaluation of the models to assess their deploy-readiness and provide insights into the difficulties of fine-grained COVID-19 multi-class classification from chest X-rays.

翻译：快速和准确的诊断对于减轻COVID-19感染的影响,特别是严重病例的影响至关重要,已经作出巨大努力,制定深层次的学习方法,对乳房射线图像中的COVID-19感染进行分类和检测,然而,最近围绕这些方法的临床可行性和有效性提出了一些问题,在这项工作中,我们调查多任务学习(分类和分解)对CNN在区分肺部COVID-19感染的各种表现的能力的影响,我们还采用自我监督的训练前方法,即Moco和Inpinting-CXR,以消除对COVID-19分类对昂贵的地面真相说明的依赖,最后,我们对这些模型进行严格评价,以评估其部署准备情况,并对精细的COVID-19从胸部X射线分类的多级分类的困难提出见解。