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

RAPID：协作研究：独立成分分析启发的统计神经网络，用于基于 3D CT 扫描的 COVID-19 边缘筛查

• 批准号: 2027539
• 负责人: Yiyu Shi
• 金额: $ 8.23万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027539&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Independent; Component

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.

COVID-19是由新型冠状病毒SARS-CoV-2引起的疾病，已导致美国和世界各地的城市关闭。由于全球缺乏用于诊断该疾病的检测试剂盒，因此首先筛查疑似患者并优先考虑那些最有可能患有COVID-19的患者进行进一步的诊断检测至关重要。由于大多数COVID-19患者在胸部计算机断层扫描（CT）图像上显示出肺炎的视觉体征，因此可以根据这些图像对患者进行筛查。然而，由于大量的疑似病例和分析3D图像所需的时间，放射科医生面临着充分筛选所有图像的挑战。最近，几项工作已经证明了深度神经网络在识别COVID-19肺炎的典型体征或部分体征方面的潜力，大大加快了筛查过程并减轻了放射科医生的负担。然而，由于与胸部CT扫描相关的大型3D体积数据（每张图像几百MB），用于分类的深度神经网络主要只在2D图像上工作，在3D CT图像上工作得不是很好。 在这个项目中，该团队探索了跨软件和硬件层的新颖解决方案，以实现一个允许即插即用的解决方案，用于快速周转的自动COVID-19筛查。该项目将使深度学习的部署能够有效和准确地筛查疑似COVID-19患者，并大大减轻放射科医生的负担。可有效解决因缺乏rRT-PCR检测试剂盒而造成的诊断瓶颈。此外，所提出的技术可以应用于COVID-19筛查以外的其他领域，在这些领域，神经网络需要处理大量数据。该项目将开放源代码，以便及时广泛分发。拟议的研究将探索ICA-Net，这是一种新颖的独立分量分析（伊卡）启发的统计神经架构，可以有效准确地从大尺寸的3D CT图像中提取特征，用于COVID-19筛查。ICA-Net将是第一个针对大型3D图像分类的神经架构。此外，考虑到该项目的实际使用，其中强烈需要患者数据的安全性/隐私性和快速周转时间，通过硬件/软件协同设计，该项目将确定最佳解决方案，使用商业现成的硬件在边缘部署，用于诊所的即插即用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。