Abstract
Coronavirus disease 2019 (COVID-19), the pandemic that is spreading fast globally, has caused over 181 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 a 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% of the total number of parameters in the baseline 3D ResNet model, our method achieves 94.8% accuracy for diagnosis, which represents a 2.4% improvement over the baseline 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.
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References
Ai, T., et al.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in china: a report of 1014 cases. Radiology 296(2), E32–E40 (2020)
Biondetti, G.P., Gauriau, R., Bridge, C.P., Lu, C., Andriole, K.P.: “Name that manufacturer”. Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT. arXiv preprint arXiv:2008.08525 (2020)
Chung, M., et al.: CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 295(1), 202–207 (2020)
Cios, K.J., Pedrycz, W., Swiniarski, R.W.: Data mining and knowledge discovery. In: Data Mining Methods for Knowledge Discovery, pp. 1–26. Springer (1998)
Fang, Y., et al.: Sensitivity of chest CT for COVID-19: Comparison to RT-PCR. Radiology 296(2), E115–E117 (2020)
Gao, H., Ji, S.: Graph U-Nets. In: International Conference on Machine Learning, pp. 2083–2092 (2019)
Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China Lancet 395(10223), 497–506 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)
Liechti, M.R., et al.: Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy. Eur. Radiol. 30(9), 4806–4815 (2020)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Ranjan, E., Sanyal, S., Talukdar, P.P.: ASAP: adaptive structure aware pooling for learning hierarchical graph representations. Proc. AAAI Conf. Artif. Intell. 34(04), 5470–5477 (2020)
Rodriguez-Morales, A.J., et al.: Clinical, laboratory and imaging features of COVID-19: a systematic review and meta-analysis. Travel Med. Infect. Dis. 34, 101623 (2020)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Advances in Neural Information Processing Systems, pp. 4800–4810 (2018)
Zhang, K., et al.: Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 181(6), 1423–1433 (2020)
Zhang, Z., et al.: Hierarchical graph pooling with structure learning. arXiv preprint arXiv:1911.05954 (2019)
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Liu, C., Cui, J., Gan, D., Yin, G. (2021). Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_27
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DOI: https://doi.org/10.1007/978-3-030-87234-2_27
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