Problem: Detecting COVID-19 from chest X-Ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19. However, the existing methods usually use supervised transfer learning from natural images as a pretraining process. These methods do not consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. Aim: In this paper, we want to design a novel high-accuracy COVID-19 detection method that uses CXR images, which can consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. Methods: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy. Results: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters.

翻译：问题：从胸部X光（CXR）图像中检测COVID-19已成为最快、最简单的检测方法之一。然而，现有方法通常采用基于自然图像的监督迁移学习作为预训练过程，未能充分考虑COVID-19的独特特征以及COVID-19与其他肺炎的相似特征。目的：本文旨在设计一种利用CXR图像的新型高精度COVID-19检测方法，该方法能够兼顾COVID-19的独特特征及其与其他肺炎的相似特征。方法：本方法包含两个阶段：一是基于自监督学习的预训练，二是基于批次知识集成的微调。自监督学习预训练无需人工标注标签即可从CXR图像中学习具有区分性的表征；而基于批次知识集成的微调则能根据批次内图像的视觉特征相似性利用其类别知识，从而提升检测性能。与既往实现不同，我们在微调阶段引入批次知识集成，减少了自监督学习阶段的内存占用，并提高了COVID-19检测精度。结果：在两种公开COVID-19 CXR数据集（即大型数据集与非平衡数据集）上，本方法展现了优异的COVID-19检测性能。即便在标注CXR训练图像显著减少（例如仅使用原始数据集的10%）的情况下，本方法仍能保持高检测精度。此外，本方法对超参数变化不敏感。