SPCXR：使用胸部X射线进行自监督预训练以构建领域特定基础模型
SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model
AI总结：
提出基于组掩蔽自监督框架的胸部X射线预训练范式，通过微调在covid-19检测、肺炎诊断及肺部分割等下游任务中表现稳健，准确率较监督方法提升约25%。
摘要：
胸部X射线（CXRs）是用于肺部疾病诊断和预后的一种广泛使用的成像模态。图像分析任务多种多样，例如病理检测和肺部分割。现有大量研究针对特定任务开发机器学习算法，一个重要的近期例子是使用CXR数据进行新型冠状病毒肺炎（covid-19）检测。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，且标注需具备良好质量以获得更好的临床结果。在此，我们提出了一种替代解决方案，即一种新的自监督范式，通过组掩蔽自监督框架从CXR中学习通用表示。预训练模型随后针对covid-19、肺炎检测和一般健康筛查等特定领域任务进行微调。我们表明，相同的预训练可用于肺部分割任务。我们提出的范式在多个下游任务中表现出稳健的性能，证明了预训练的成功。此外，预训练模型在测试期间具有显著数据漂移的数据上的表现，证明了其学习到了更好的通用表示。该方法进一步通过在独特的小规模儿科数据集上进行covid-19检测得到验证。与基于监督的transformer方法相比，准确率方面的性能提升（约25%）是显著的。这增加了我们提出的框架和预训练策略的强度和可靠性的可信度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
