We are concerned with the challenge of coronavirus disease (COVID-19) detection in chest X-ray and Computed Tomography (CT) scans, and the classification and segmentation of related infection manifestations. Even though it is arguably not an established diagnostic tool, using machine learning-based analysis of COVID-19 medical scans has shown the potential to provide a preliminary digital second opinion. This can help in managing the current pandemic, and thus has been attracting significant research attention. In this research, we propose a multi-task pipeline that takes advantage of the growing advances in deep neural network models. In the first stage, we fine-tuned an Inception-v3 deep model for COVID-19 recognition using multi-modal learning, that is, using X-ray and CT scans. In addition to outperforming other deep models on the same task in the recent literature, with an attained accuracy of 99.4%, we also present comparative analysis for multi-modal learning against learning from X-ray scans alone. The second and the third stages of the proposed pipeline complement one another in dealing with different types of infection manifestations. The former features a convolutional neural network architecture for recognizing three types of manifestations, while the latter transfers learning from another knowledge domain, namely, pulmonary nodule segmentation in CT scans, to produce binary masks for segmenting the regions corresponding to these manifestations. Our proposed pipeline also features specialized streams in which multiple deep models are trained separately to segment specific types of infection manifestations, and we show the significant impact that this framework has on various performance metrics. We evaluate the proposed models on widely adopted datasets, and we demonstrate an increase of approximately 2.5% and 4.5% for dice coefficient and mean intersection-over-union (mIoU), respectively, while achieving 60% reduction in computational time, compared to the recent literature.

中文翻译：

---

具有专门流的多任务管道，用于对 COVID-19 扫描中的感染表现进行分类和分割

我们关注胸部 X 光和计算机断层扫描 (CT) 扫描中冠状病毒病 (COVID-19) 检测的挑战，以及相关感染表现的分类和分割。尽管它可以说不是一种成熟的诊断工具，但使用基于机器学习的 COVID-19 医学扫描分析已显示出提供初步数字第二意见的潜力。这有助于控制当前的大流行，因此引起了广泛的研究关注。在这项研究中，我们提出了一种多任务管道，利用深度神经网络模型的不断进步。在第一阶段，我们使用多模态学习（即使用 X 射线和 CT 扫描）对用于 COVID-19 识别的 Inception-v3 深度模型进行了微调。除了在最近的文献中在同一任务上优于其他深度模型（达到 99.4% 的准确率）之外，我们还提出了多模态学习与单独从 X 射线扫描学习的比较分析。拟议管道的第二阶段和第三阶段在处理不同类型的感染表现方面相互补充。前者采用卷积神经网络架构来识别三种类型的表现，而后者则从另一个知识领域（即 CT 扫描中的肺结节分割）转移学习，以生成用于分割与这些表现相对应的区域的二进制掩模。我们提出的管道还具有专门的流，其中单独训练多个深度模型以分割特定类型的感染表现，并且我们展示了该框架对各种性能指标的重大影响。我们在广泛采用的数据集上评估了所提出的模型，结果表明，与之前的模型相比，dice 系数和平均交并集 (mIoU) 分别增加了约 2.5% 和 4.5%，同时计算时间减少了 60%最近的文献。