Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Lear

Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning

Coronavirus disease 2019 (COVID-19), the pandemic that is spreading fast globally, has caused over 34 million confirmed cases. Apart from the reverse transcription polymerase chain reaction (RT-PCR), the chest computed tomography (CT) is viewed as a standard and effective tool for disease diagnosis and progression monitoring.We propose a diagnosis and prognosis model based on graph convolutional networks (GCNs). The chest CT scan of a patient, typically involving hundreds of sectional images in sequential order, is formulated as a densely connected weighted graph. A novel distance aware pooling is proposed to abstract the node information hierarchically, which is robust and efficient for such densely connected graphs. Our method, combining GCNs and distance aware pooling, can integrate the information from all slices in the chest CT scans for optimal decision making, which leads to the state-of-the-art accuracy in the COVID-19 diagnosis and prognosis. With less than 1% number of total parameters in the baseline 3D ResNet model, our method achieves 94.7% accuracy for diagnosis. It has a 2.3% improvement compared with the baseline model on the same dataset. In addition, we can localize the most informative slices with disease lesions for COVID-19 within a large sequence of chest CT images. The proposed model can produce visual explanations for the diagnosis and prognosis, making the decision more transparent and explainable, while RT-PCR only leads to the test result with no prognosis information. The prognosis analysis can help hospitals or clinical centers designate medical resources more efficiently and better support clinicians to determine the proper clinical treatment.

超越COVID-19诊断：分层图表示学习的预后

冠状病毒病2019（COVID-19）是一种在全球范围内迅速蔓延的大流行病，已导致超过3400万例确诊病例。除逆转录聚合酶链反应（RT-PCR）外，胸部计算机断层扫描（CT）被视为疾病诊断和进展监测的标准有效工具。.. 我们提出了基于图卷积网络（GCN）的诊断和预后模型。通常将患者的胸部CT扫描按顺序包含数百个断层图像，并绘制为密集连接的加权图。提出了一种新颖的距离感知池，以分层地抽象节点信息，对于这种密集连接的图而言，鲁棒和高效。我们的方法结合了GCN和距离感知池，可以将来自胸部CT扫描中所有切片的信息整合在一起，以做出最佳决策，从而使COVID-19诊断和预后达到最新水平。在基线3D ResNet模型中，总参数的数量少于1％，我们的方法可实现94.7％的诊断准确性。与同一数据集上的基线模型相比，它具有2.3％的改进。此外，我们可以在大量的胸部CT图像中定位出最有用的COVID-19病灶切片。所提出的模型可以为诊断和预后提供直观的解释，使决策更加透明和可解释，而RT-PCR仅导致测试结果，而没有预后信息。预后分析可以帮助医院或临床中心更有效地指定医疗资源，并更好地支持临床医生确定正确的临床治疗方法。 （阅读更多）