论文标题
使用多源传输学习将COVID-19在CT扫描中分类
Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning
论文作者
论文摘要
自2019年12月以来，新型冠状病毒疾病Covid-19在世界各地蔓延，感染了数百万的人并振奋全球经济。高感染率背后的驱动原因之一是由于不可靠和缺乏RT-PCR测试所致。有时，周转结果范围为几天，仅产生了大约70％的灵敏度。作为替代方案，最近的研究调查了计算机视觉与卷积神经网络（CNN）从CT扫描中对COVID-19进行分类。由于缺乏可用的COVID-19 CT数据，这些研究工作被迫利用转移学习的使用。这种常用的深度学习技术已证明可以改善具有相对较少数据的任务的模型性能，只要源特征空间与目标特征空间有些相似。不幸的是，在医学图像的分类中经常遇到缺乏相似性，因为公开可用的源数据集通常缺乏医学图像中发现的视觉特征。在这项研究中，我们建议使用多源转移学习（MSTL）来改进传统转移学习，以从CT扫描中对COVID-19进行分类。通过我们的多源微调方法，我们的模型优于用成像网微调的基线模型。此外，我们提出了一个无监督的标签创建过程，从而增强了我们深层剩余网络的性能。我们最佳性能模型能够达到0.893的精度和0.897的召回得分，其表现优于其基线召回得分9.3％。
Since December of 2019, novel coronavirus disease COVID-19 has spread around the world infecting millions of people and upending the global economy. One of the driving reasons behind its high rate of infection is due to the unreliability and lack of RT-PCR testing. At times the turnaround results span as long as a couple of days, only to yield a roughly 70% sensitivity rate. As an alternative, recent research has investigated the use of Computer Vision with Convolutional Neural Networks (CNNs) for the classification of COVID-19 from CT scans. Due to an inherent lack of available COVID-19 CT data, these research efforts have been forced to leverage the use of Transfer Learning. This commonly employed Deep Learning technique has shown to improve model performance on tasks with relatively small amounts of data, as long as the Source feature space somewhat resembles the Target feature space. Unfortunately, a lack of similarity is often encountered in the classification of medical images as publicly available Source datasets usually lack the visual features found in medical images. In this study, we propose the use of Multi-Source Transfer Learning (MSTL) to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. We additionally, propose an unsupervised label creation process, which enhances the performance of our Deep Residual Networks. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
