Silva Pedro, Luz Eduardo, Silva Guilherme, Moreira Gladston, Silva Rodrigo, Lucio Diego, Menotti David
Computing Department, Universidade Federal de Ouro Preto (UFOP), MG, Brazil.
Department of Control and Automation Engineering, Universidade Federal de Ouro Preto (UFOP), MG, Brazil.
Inform Med Unlocked. 2020;20:100427. doi: 10.1016/j.imu.2020.100427. Epub 2020 Sep 14.
Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. These approaches, however, suffer from at least one of the following problems: (i) they treat each CT scan slice independently and (ii) the methods are trained and tested with sets of images from the same dataset. Treating the slices independently means that the same patient may appear in the training and test sets at the same time which may produce misleading results. It also raises the question of whether the scans from the same patient should be evaluated as a group or not. Moreover, using a single dataset raises concerns about the generalization of the methods. Different datasets tend to present images of varying quality which may come from different types of CT machines reflecting the conditions of the countries and cities from where they come from. In order to address these two problems, in this work, we propose an Efficient Deep Learning Technique for the screening of COVID-19 with a voting-based approach. In this approach, the images from a given patient are classified as group in a voting system. The approach is tested in the two biggest datasets of COVID-19 CT analysis with a patient-based split. A cross dataset study is also presented to assess the robustness of the models in a more realistic scenario in which data comes from different distributions. The cross-dataset analysis has shown that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario. These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.
早期检测和诊断是控制新冠病毒传播的关键因素。最近，人们提出了一些基于深度学习的方法用于在CT扫描中筛查新冠病毒，作为一种自动化并辅助诊断的工具。然而，这些方法至少存在以下问题之一：（i）它们独立处理每个CT扫描切片；（ii）这些方法使用同一数据集的图像集进行训练和测试。独立处理切片意味着同一患者可能同时出现在训练集和测试集中，这可能产生误导性结果。这也引发了一个问题，即来自同一患者的扫描是否应作为一个整体进行评估。此外，使用单一数据集引发了对这些方法泛化能力的担忧。不同的数据集往往呈现质量各异的图像，这些图像可能来自不同类型的CT机器，反映了其来源国家和城市的情况。为了解决这两个问题，在这项工作中，我们提出了一种基于投票的高效深度学习技术用于新冠病毒筛查。在这种方法中，给定患者的图像在投票系统中作为一个整体进行分类。该方法在两个最大的新冠病毒CT分析数据集中以基于患者的划分方式进行测试。还进行了跨数据集研究，以在更现实的场景（其中数据来自不同分布）中评估模型的稳健性。跨数据集分析表明，深度学习模型的泛化能力对于该任务而言远远不能令人接受，因为在最佳评估场景下，准确率从87.68%降至56.16%。这些结果凸显出，旨在通过CT图像检测新冠病毒的方法必须大幅改进才能被视为一种临床选择，并且需要更大且更多样化的数据集来在现实场景中评估这些方法。
