论文标题
基于自我蒸馏的基于自我监督的学习，用于从胸部X射线图像检测的共同蒸馏
Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images
论文作者
论文摘要
2019年冠状病毒（COVID-19）全球爆发的全球医疗保健系统超负荷。 COVID-19的计算机辅助诊断快速检测和患者分类变得至关重要。本文提出了一种基于自我监督的新型自我蒸馏的新型学习方法，用于从胸部X射线图像中检测到COVID-19。我们的方法可以根据其视觉特征的相似性来利用图像的自我知识进行自我监督的学习。实验结果表明，我们的方法的HM得分为0.988，AUC为0.999，在最大的开放式Covid-19胸部X射线数据集上的精度为0.957。
The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast detection and patient triage is becoming critical. This paper proposes a novel self-knowledge distillation based self-supervised learning method for COVID-19 detection from chest X-ray images. Our method can use self-knowledge of images based on similarities of their visual features for self-supervised learning. Experimental results show that our method achieved an HM score of 0.988, an AUC of 0.999, and an accuracy of 0.957 on the largest open COVID-19 chest X-ray dataset.
