ComputeCOVID19+: Accelerating COVID-19 Diagnosis and Monitoring via High-Performance Deep Learning on CT Images
ComputeCOVID19+: Accelerating COVID-19 Diagnosis and Monitoring via High-Performance Deep Learning on CT Images
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DOI：
10.1145/3472456.3473523
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Garvit Goel;Atharva Gondhalekar;Jingyuan Qi;Zhicheng Zhang;Guohua Cao;Wuna Feng
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文献类型：
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作者：
Garvit Goel;Atharva Gondhalekar;Jingyuan Qi;Zhicheng Zhang;Guohua Cao;Wuna Feng
新冠肺炎疫情凸显了及早准确诊断和监测的重要性。然而，逆转录-聚合酶链式反应(RT-PCR)测试的结果有两个问题：(1)从样本采集到检测结果的周转时间很长；(2)由于样本收集、包装和交付到实验室进行RT-PCR测试的时间和方式，测试准确性降低，最低可达67%。因此，我们提出了ComputeCOVID19+，这是我们基于计算机断层扫描的框架，通过基于深度学习的CT图像增强网络DDNet(DenseNet和去卷积网络的缩写)来提高新冠肺炎(及其变体)的测试速度和精度。为了证明其速度和准确性，我们在多个计算机断层扫描(CT)图像源和许多不同的平台上对ComputeCOVID19+进行了评估，包括多核CPU、多核GPU，甚至是FPGA。测试结果表明，ComputeCOVID19+可以将测试周期从几天缩短到几分钟，并将测试准确率提高到91%。
The COVID-19 pandemic has highlighted the importance of diagnosis and monitoring as early and accurately as possible. However, the reverse-transcription polymerase chain reaction (RT-PCR) test results in two issues: (1) protracted turnaround time from sample collection to testing result and (2) compromised test accuracy, as low as 67%, due to when and how the samples are collected, packaged, and delivered to the lab to conduct the RT-PCR test. Thus, we present ComputeCOVID19+, our computed tomography-based framework to improve the testing speed and accuracy of COVID-19 (plus its variants) via a deep learning-based network for CT image enhancement called DDnet, short for DenseNet and Deconvolution network. To demonstrate its speed and accuracy, we evaluate ComputeCOVID19+ across several sources of computed tomography (CT) images and on many heterogeneous platforms, including multi-core CPU, many-core GPU, and even FPGA. Our results show that ComputeCOVID19+ can significantly shorten the turnaround time from days to minutes and improve the testing accuracy to 91%.