RAPID:Collaborative:Independent Component Analysis Inspired Statistical Neural Networks for 3D CT Scan Based Edge Screening of COVID-19

由新型冠状病毒SARS-CoV-2引起的疾病新冠肺炎已经关闭了美国和世界各地的城市。由于全球缺乏用于诊断这种疾病的检测试剂盒，至关重要的是首先对疑似患者进行筛查，并优先考虑那些最有可能患有新冠肺炎的患者进行进一步的诊断测试。由于大多数新冠肺炎患者在胸部计算机断层扫描(CT)的图像上显示出肺炎的视觉迹象，因此基于这些图像进行患者筛查是可能的。然而，由于疑似病例数量众多，分析3D图像需要时间，放射科医生面临着对所有图像进行充分筛查的挑战。最近，几项工作展示了深度神经网络在识别新冠肺炎肺炎的典型体征或部分体征、大幅加快筛查过程和减轻放射科医生负担方面的潜力。然而，由于与胸部CT扫描相关的大三维体数据(每幅图像几百MB)，用于分类的深度神经网络通常仅适用于2D图像，对于3D CT图像并不是很好地工作。在这个项目中，团队探索了跨越软件和硬件层的新颖解决方案，以实现一个即插即用的解决方案，以实现快速周转的自动新冠肺炎筛选。该项目将使深度学习的部署能够高效、准确地筛查疑似新冠肺炎患者，并显著减轻放射科医生的负担。它可以有效地解决由于缺乏RRT-PCR检测试剂盒而造成的诊断瓶颈。此外，所提出的技术还可以应用于新冠肺炎筛选之外的其他领域，即神经网络需要处理大数据量的领域。该项目将开源，以实现及时的广泛分发。拟议的研究将探索ICA-Net，一种受独立成分分析(ICA)启发的新型统计神经架构，可以高效准确地从大尺寸CT图像中提取特征，用于新冠肺炎筛查。ICA-Net将是第一个面向大体积3D图像分类的神经体系结构。此外，考虑到该项目的实际应用非常需要患者数据的安全/隐私和快速周转时间，通过硬件/软件协同设计，该项目将确定在边缘部署最佳解决方案，使用商业上现成的硬件，用于诊所的即插即用。因此，它可以立即整合并用于新冠肺炎筛选。该奖项反映了美国国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

COVID-19, the disease caused by the new coronavirus SARS-CoV-2, has shut down cities in the United State and around the world. Due to the global lack of test kits used to diagnose the disease, it is critical to screen suspected patients first and prioritize those most likely to have COVID-19 for further diagnostic test. As most patients with COVID-19 show visual signs of the pneumonia on images from chest Computerized Tomography (CT) scans, it is possible to screen patients based on these images. However, with the large number of suspected cases and the time required to analyze 3D images, radiologists are challenged to adequately screen all of the images. Most recently, several works have demonstrated the potential of deep neural networks in identifying typical signs or partial signs of COVID-19 pneumonia, drastically speeding up the screening process and reducing the burden on radiologists. Due to the large 3D volumetric data associated with chest CT scans (a few hundred MB per image), however, the deep neural networks for classification, which mostly work on 2D images only, do not work very well on 3D CT images. In this project, , the team explores novel solutions across software and hardware layers to enable a solution that allows plug-and-play for automatic COVID-19 screening with fast turn-around time. The project will enable the deployment of deep learning to efficiently and accurately screen suspected COVID-19 patients, and significantly reduce the burden on radiologists. It can effectively address the diagnosis bottleneck caused by the lack of rRT-PCR test kits. In addition, the proposed techniques can be applied to other areas beyond COVID-19 screening where neural networks need to handle large volumetric data. The project will be made open source to enable wide distribution in a timely manner.The proposed research will explore ICA-Net, a novel Independent Component Analysis (ICA) inspired statistical neural architecture that can efficiently and accurately extract features from 3D CT images of large sizes for COVID-19 screening. ICA-Net will be the first neural architecture that targets large volumetric 3D image classification. In addition, considering the practical use of this project where security/privacy of patient data and fast turn-around time are strongly desired, through hardware/software co-design, the project will identify the best solution to be deployed on the edge using commercially off-the-shelf hardware for plug-and-play in clinics. As such, it can be immediately integrated and used for COVID-19 screening.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.