[论文解读] COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs

Muhammad Ali Farooq, Abdul Hafeez|arXiv (Cornell University)|Mar 31, 2020

COVID-19 diagnosis using AI参考文献 13被引用 438

一句话总结

本论文提出 COVIDResNet，这是对预训练 ResNet-50 的一个开源3步微调，用于将 COVID-19、其他肺炎和正常胸部X光片进行分类，在COVIDx数据集上通过渐进式调整大小和判别学习率达到最先进的准确率。

ABSTRACT

In the last few months, the novel COVID19 pandemic has spread all over the world. Due to its easy transmission, developing techniques to accurately and easily identify the presence of COVID19 and distinguish it from other forms of flu and pneumonia is crucial. Recent research has shown that the chest Xrays of patients suffering from COVID19 depicts certain abnormalities in the radiography. However, those approaches are closed source and not made available to the research community for re-producibility and gaining deeper insight. The goal of this work is to build open source and open access datasets and present an accurate Convolutional Neural Network framework for differentiating COVID19 cases from other pneumonia cases. Our work utilizes state of the art training techniques including progressive resizing, cyclical learning rate finding and discriminative learning rates to training fast and accurate residual neural networks. Using these techniques, we showed the state of the art results on the open-access COVID-19 dataset. This work presents a 3-step technique to fine-tune a pre-trained ResNet-50 architecture to improve model performance and reduce training time. We call it COVIDResNet. This is achieved through progressively re-sizing of input images to 128x128x3, 224x224x3, and 229x229x3 pixels and fine-tuning the network at each stage. This approach along with the automatic learning rate selection enabled us to achieve the state of the art accuracy of 96.23% (on all the classes) on the COVIDx dataset with only 41 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of three different infection types from along with Normal individuals. This model can help in the early screening of COVID19 cases and help reduce the burden on healthcare systems.

研究动机与目标

• 动机：需要对胸部放射线影像中的 COVID-19 进行准确、可及的筛查。
• 提供一个开源、可重复的数据集和模型，用于区分 COVID-19、其他肺炎和正常病例。
• 开发一个 3 步微调策略，以提高性能并减少训练时间。

提出的方法

• 使用预训练的 ResNet-50 主干，并通过将输入进行 3 步渐进式调整大小到 128x128x3、224x224x3 和 229x229x3 来微调。
• 将渐进式调整大小与自动学习率选择相结合以优化训练。
• 实现判别学习率，以在微调期间定制逐层更新。
• 利用开放获取的 COVIDx 数据集进行三种感染类型与正常病例的多类别分类。
• 目标是在有限的训练周期内实现计算高效和高准确性。

实验结果

研究问题

• RQ1 基于 ResNet-50 的架构是否能够在胸部放射影像中区分 COVID-19、其他肺炎和正常病例？
• RQ2 渐进式调整大小结合判别学习率是否提升了 COVIDx 数据集上的训练效率和准确性？
• RQ3 在对预训练网络进行多类别 COVID-19 筛查微调时，可以达到的准确性和训练效率是多少？

主要发现

• 在 COVIDx 数据集的所有类别上达到 96.23% 的最先进准确率。
• 为达到所报告的性能，使用了 41 个训练周期。
• 展示了一种在感染类型与正常病例的多类别分类中计算上高效的方法。
• 提供了一个 3 步微调方案，在每个阶段渐进式调整输入大小并对网络进行调整。
• 突出开源和开放获取的数据与模型，便于复现和社区使用。