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; 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是由新的冠状病毒SARS-COV-2引起的疾病，已关闭了美国和世界各地的城市。由于全球缺乏用于诊断该疾病的测试试剂盒，因此首先要筛查可疑患者并优先考虑最有可能具有COVID-19的患者进行进一步诊断测试。由于大多数Covid-19患者在胸部计算机断层扫描（CT）扫描中显示出肺炎的视觉迹象，因此可以根据这些图像筛选患者。但是，随着大量可疑情况以及分析3D图像所需的时间，放射科医生面临充分筛选所有图像的挑战。最近，一些作品证明了深层神经网络在识别Covid-19肺炎的典型迹象或部分迹象方面的潜力，从而大大加快了筛查过程并减轻了放射科医生的负担。但是，由于与胸部CT扫描相关的大量3D体积数据（每图像几百MB），因此，用于分类的深神经网络（主要用于2D图像），在3D CT图像上不能很好地工作。 在这个项目中，团队探索了跨软件和硬件层的新颖解决方案，以启用一种解决方案，该解决方案允许使用快速的转弯时间进行自动互联-19筛选。该项目将使深度学习的部署能够有效，准确地筛查可疑的1900患者，并显着减轻放射科医生的负担。它可以有效地解决由于缺乏RRT-PCR测试套件而引起的诊断瓶颈。此外，提出的技术可以应用于Covid-19筛选以外的其他领域，在该区域中，神经网络需要处理大量的大量数据。该项目将成为开源的，以及时启用广泛的分布。拟议的研究将探索ICA-NET，这是一种新型的独立组件分析（ICA）启发的统计神经体系结构，可以有效，准确地从3D CT图像中提取大小的3D CT图像，以进行共同-19筛选。 ICA-NET将是针对大容量3D图像分类的第一个神经体系结构。此外，考虑到该项目的实际使用，通过硬件/软件共同设计，强烈希望患者数据的安全/隐私时间和快速的转折时间，该项目将确定最佳的解决方案，该解决方案将使用商业上销售的硬件在诊所插入式销售。因此，它可以立即将其用于COVID-19筛查。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的审查标准，认为值得通过评估来获得支持。