BEYOND COVID-19 DIAGNOSIS: PROGNOSIS WITH HIERARCHICAL GRAPH REPRESENTATION LEARNING Anonymous authors Paper under double-blind review

Abstract

Coronavirus disease 2019 , 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 94.8% accuracy for diagnosis. It has a 2.4% 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.

1. INTRODUCTION

Coronavirus disease 2019 has resulted in an ongoing pandemic in the world. To control the sources of infection and cut off the channels of transmission, rapid testing and detection are of vital importance. The reverse transcription polymerase chain reaction (RT-PCR) is a widely-used screening technology and viewed as the standard method for suspected cases. However, this method highly relies upon the required lab facilities and the diagnostic kits. In addition, the sensitivity of RT-PCR is not high enough for early diagnosis (Ai et al., 2020; Fang et al., 2020) . To mitigate the limitations of RT-PCR, the computed tomography (CT) has been widely used as an effective complementary method, which can provide medical images of the lung area to reveal the details of the disease and its prognosis (Huang et al., 2020; Chung et al., 2020) , for which RT-PCR cannot. Additionally, CT has also been proved to be useful in monitoring the COVID-19 disease progression and the therapeutic efficacy evaluation (Rodriguez-Morales et al., 2020; Liechti et al., 2020) . The chest CT slices of a patient have a sequential and hierarchical data structure. The relationship between slices possesses more information than the order of the slices. The adjacent ones with the same abnormality could be considered as one lesion. The slices containing the same type of lesions may not be continuous as the lesions are distributed in various lung parts. We propose a diagnosis and prognosis system that combines graph convolutional networks (GCNs) and a distance aware pooling, which integrates the information from all slices in the chest CT scans for optimal decision making. Our major contributions are three-fold: (1) Owing to the sequential structure of CT images, this is the first work to utilize GCNs to extract node information hierarchically, and conduct both diagnosis and prognosis for COVID-19. The prognosis can help facilitate medical resources, e.g., ventilators or admission to Intensive Care Units (ICUs), more efficiently by triaging

