COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction

The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity. Machine learning techniques using clinical data such as chest X-rays have been used to predict which patients are most at risk of deterioration. We consider the task of predicting two types of patient deterioration based on chest X-rays: adverse event deterioration (i.e., transfer to the intensive care unit, intubation, or mortality) and increased oxygen requirements beyond 6 L per day. Due to the relative scarcity of COVID-19 patient data, existing solutions leverage supervised pretraining on related non-COVID images, but this is limited by the differences between the pretraining data and the target COVID-19 patient data. In this paper, we use self-supervised learning based on the momentum contrast (MoCo) method in the pretraining phase to learn more general image representations to use for downstream tasks. We present three results. The first is deterioration prediction from a single image, where our model achieves an area under receiver operating characteristic curve (AUC) of 0.742 for predicting an adverse event within 96 hours (compared to 0.703 with supervised pretraining) and an AUC of 0.765 for predicting oxygen requirements greater than 6 L a day at 24 hours (compared to 0.749 with supervised pretraining). We then propose a new transformer-based architecture that can process sequences of multiple images for prediction and show that this model can achieve an improved AUC of 0.786 for predicting an adverse event at 96 hours and an AUC of 0.848 for predicting mortalities at 96 hours. A small pilot clinical study suggested that the prediction accuracy of our model is comparable to that of experienced radiologists analyzing the same information.

https://arxiv.org/abs/2101.04909

COVID-19的快速传播让医疗资源变得紧张,所以对病人进行准确而快速的分诊是必要的。使用机器学习方法处理例如胸部X光的诊疗数据已经被广泛采用。我们提出采用胸部X光数据将病人分为两类:继续恶化(移交重症监护病房,插管治疗或者死亡)和提高氧气供给至少6L每天。因为目前较为缺乏COVID-19的病人数据,现有的诊断方法往往依赖在非COVID-19的病例上进行监督预训练。在本文中,我们使用基于动量对比(MoCo)的自监督学习方法以在预训练阶段学习更多泛化的图像表示以用于下游任务中。我们展示了三个结果。第一个是恶化程度的预测结果,这个结果是从单张图片中获得的。我们的模型在预测接下来96小时的恶化事件的任务中获得了0.742的AUC,以及在接下来24小时的氧气供给量超过6L事件的任务中获得了0.749的AUC.然后我们还提出了一种新的基于Transformer的模型用来处理图片序列,通过这个模型我们对于死亡事件的预测AUC提高到0.848。在较小的实验数据集上测试的结果显示我们的模型可以达到有经验的放射学家的诊断水平。

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