冠状病毒病(COVID-19)已经引起了全球大流行,将数百万人的健康和生命置于危险之中。在胸部计算机断层扫描(CT)上早期检测感染患者对于对抗COVID-19至关重要。利用不确定性感知共识辅助多实例学习(UC-MIL),我们建议使用一种新的双边自适应图(BA-GCN)模型来诊断COVID-19,该模型可以在任意数量切片的3DCT体积中同时使用2D和3D判别信息.鉴于肺分割对这项任务的重要性,我们已经创建了迄今为止最大的手动注释数据集,共有来自COVID-19患者的7768个切片,并使用它来训练2D分割模型,以从单个切片中分割肺部,并将肺部掩盖为后续分析的感兴趣区域。然后,我们使用UC-MIL模型来估计每个预测的不确定性以及每个CT切片上多个预测之间的共识,以自动选择具有可靠预测的固定数量的CT切片,用于后续的模型推理。最后,我们自适应地构造了一个具有不同粒度级别(2D和3D)的顶点的BA-GCN,以聚合多级特征进行最终诊断,这得益于图卷积网络处理跨粒度关系的优越性。在三个最大的COVID-19CT数据集上的实验结果表明,我们的模型可以使用具有任意数量切片的CT体积来产生可靠和准确的COVID-19预测,在学习和泛化能力方面优于现有方法。为了促进可重复的研究,我们制作了数据集,包括手动注释和清理的CT数据集,以及实现代码,可在https://doi.org/10.5281/zenodo.6361963.
Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people\'s health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical in combating COVID-19. Harnessing uncertainty-aware
consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using a new bilateral adaptive graph-based (BA-GCN) model that can use both 2D and 3D discriminative information in 3D CT volumes with arbitrary number of slices. Given the importance of lung segmentation for this task, we have created the largest manual annotation dataset so far with 7,768 slices from COVID-19 patients, and have used it to train a 2D segmentation model to segment the lungs from individual slices and mask the lungs as the regions of interest for the subsequent analyses. We then used the UC-MIL model to estimate the uncertainty of each prediction and the
consensus between multiple predictions on each CT slice to automatically select a fixed number of CT slices with reliable predictions for the subsequent model reasoning. Finally, we adaptively constructed a BA-GCN with vertices from different granularity levels (2D and 3D) to aggregate multi-level features for the final diagnosis with the benefits of the graph convolution network\'s superiority to tackle cross-granularity relationships. Experimental results on three largest COVID-19 CT datasets demonstrated that our model can produce reliable and accurate COVID-19 predictions using CT volumes with any number of slices, which outperforms existing approaches in terms of learning and generalisation ability. To promote reproducible research, we have made the datasets, including the manual annotations and cleaned CT dataset, as well as the implementation code, available at https://doi.org/10.5281/zenodo.6361963.