Chest X-rays (CXRs) are a widely used imaging modality for the diagnosis and prognosis of lung disease. The image analysis tasks vary. Examples include pathology detection and lung segmentation. There is a large body of work where machine learning algorithms are developed for specific tasks. A significant recent example is Coronavirus disease (covid-19) detection using CXR data. However, the traditional diagnostic tool design methods based on supervised learning are burdened by the need to provide training data annotation, which should be of good quality for better clinical outcomes. Here, we propose an alternative solution, a new self-supervised paradigm, where a general representation from CXRs is learned using a group-masked self-supervised framework. The pre-trained model is then fine-tuned for domain-specific tasks such as covid-19, pneumonia detection, and general health screening. We show that the same pre-training can be used for the lung segmentation task. Our proposed paradigm shows robust performance in multiple downstream tasks which demonstrates the success of the pre-training. Moreover, the performance of the pre-trained models on data with significant drift during test time proves the learning of a better generic representation. The methods are further validated by covid-19 detection in a unique small-scale pediatric data set. The performance gain in accuracy (~25%) is significant when compared to a supervised transformer-based method. This adds credence to the strength and reliability of our proposed framework and pre-training strategy.

翻译：胸部X光片（CXR）是用于肺部疾病诊断与预后评估的广泛使用的影像学模态。图像分析任务各不相同，例如病理检测和肺部分割。现有大量研究工作针对特定任务开发机器学习算法，近期显著案例包括利用CXR数据检测冠状病毒病（COVID-19）。然而，基于监督学习的传统诊断工具设计方法受限于需要提供训练数据标注，且为获得更好的临床结果，标注质量需较高。本文提出一种替代方案——新的自监督范式，通过分组掩码自监督框架从CXR中学习通用表征。预训练模型随后针对领域特定任务（如COVID-19、肺炎检测及常规健康筛查）进行微调。我们表明，同一预训练模型也可用于肺部分割任务。所提范式在多个下游任务中展现出稳健性能，证明了预训练的成功。此外，预训练模型在测试阶段数据显著漂移时的表现，证实了其对更优通用表征的学习能力。通过独特的小规模儿科数据集进行COVID-19检测，进一步验证了该方法。与基于监督式Transformer的方法相比，准确率提升约25%的显著性能增益，进一步佐证了所提框架及预训练策略的优越性与可靠性。