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RAPID: Deep Learning Models for Early Screening of COVID-19 using CT Images

RAPID：使用 CT 图像进行 COVID-19 早期筛查的深度学习模型

基本信息

批准号：
2027628
负责人：
Aryya Gangopadhyay
金额：
$ 15万
依托单位：
University of Maryland Baltimore County
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：
2020-06-01 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027628&HistoricalAwards=false
关键词：
RAPID Deep Learning Models Early

项目摘要

The rapid spread of COVID-19 has severely impacted the lives of billions of people across the world. Healthcare systems are strained both in terms of dealing with the large number of cases but also the risk of infection imposed on healthcare workers. This project develops low-cost, effective, and minimal contact early screening tools for detection, treatment, and prevention of the spread of the disease. In order to respond to infectious diseases such as COVID-19 and prevent future, this project proactively builds resources to help the medical community be better prepared in early stages of diseases with pandemic potential. This project develops an understanding of SARS-CoV-2 through an early screening tool to distinguish the recent coronavirus (COVID-19) infections from other respiratory illnesses such as Influenza-A and viral or bacterial pneumonia as well as from patients who have no pulmonary disease. There are two major contributions of the project: (1) generate high quality Convolutional Neural Networks (CNNs) with 2D and 3D kernels for early detection of COVID-19 infection, and (2) synthesize realistic Computed Tomography (CT) images using Generative Adversarial Networks (GANs) that will be publicly available for research and practice.The project has significant broader impacts in the United States and across the globe. The pre-trained models are useful as early screening tools by medical practitioners. The pre-trained models can also be useful in studying other widespread diseases and pandemics in the future. The synthetic data generated in this project allows researchers to develop newer models for early screening of COVID-19. This project will be part of the necessary preparation that the United States and other nations across the world could put in place to minimize the impact of future disasters caused by pandemic diseases such as COVID-19. This project is being performed within the auspices of the Center for Accelerated Real Time Analytics (CARTA), an Industry University Cooperative Research Center at UMBC funded by NSF. The project repository will be maintained at https://carta.umbc.edu/ for 5 years. The repository consists of the following resources: (1) High-quality pre-trained models for early detection of COVID-19 detection and (2) realistic CT images with both 2D axial slices and 3D volumes that can be used to train other models. The codes, models, synthetic data, and results generated in this project are being widely disseminated through the project website and Github repository.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的迅速蔓延严重影响了全球数十亿人的生活。医疗保健系统在处理大量病例方面以及对医疗保健工作者施加的感染风险方面都很紧张。该项目开发了低成本、有效和最小接触的早期筛查工具，用于检测、治疗和预防疾病的传播。为应对COVID-19等传染病并预防未来，该项目积极建立资源，以帮助医疗界在具有大流行潜力的疾病的早期阶段做好更好的准备。该项目通过早期筛查工具来了解SARS-CoV-2，以区分最近的冠状病毒（COVID-19）感染与其他呼吸道疾病，如流感-A和病毒或细菌性肺炎以及没有肺部疾病的患者。该项目有两个主要贡献：（1）生成具有2D和3D内核的高质量卷积神经网络（CNN），用于早期检测COVID-19感染，以及（2）使用生成对抗网络（GANs）合成逼真的计算机断层扫描（CT）图像，该网络将公开用于研究和实践。该项目在美国和地球仪产生了广泛的影响。预先训练的模型是有用的早期筛查工具，由医疗从业者。预先训练的模型也可以用于研究未来其他广泛传播的疾病和流行病。该项目产生的合成数据使研究人员能够开发用于COVID-19早期筛查的更新模型。该项目将是美国和世界其他国家为最大限度地减少COVID-19等大流行病造成的未来灾难的影响而进行的必要准备的一部分。该项目正在加速真实的时间分析中心（CARTA）的主持下进行，该中心是由NSF资助的UMBC工业大学合作研究中心。项目存储库将在https://carta.umbc.edu/上维护5年。该存储库由以下资源组成：（1）用于早期检测COVID-19检测的高质量预训练模型和（2）可用于训练其他模型的具有2D轴向切片和3D体积的逼真CT图像。该项目产生的代码、模型、合成数据和结果通过项目网站和Github存储库广泛传播。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。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Generating Realistic COVID-19 x-rays with a Mean Teacher + Transfer Learning GAN

DOI：
10.1109/bigdata50022.2020.9377878
发表时间：
2020-09
期刊：
2020 IEEE International Conference on Big Data (Big Data)
影响因子：
0
作者：
Sumeet Menon;Joshua Galita;David Chapman;A. Gangopadhyay;Jayalakshmi Mangalagiri;Phuong Nguyen;Y. Yesha;Y. Yesha;B. Saboury;Michael Morris
通讯作者：
Sumeet Menon;Joshua Galita;David Chapman;A. Gangopadhyay;Jayalakshmi Mangalagiri;Phuong Nguyen;Y. Yesha;Y. Yesha;B. Saboury;Michael Morris

Classification of COVID-19 using Deep Learning and Radiomic Texture Features extracted from CT scans of Patients Lungs

使用深度学习和从患者肺部 CT 扫描中提取的放射纹理特征对 COVID-19 进行分类

DOI：
10.1109/bigdata52589.2021.9671656
发表时间：
2021
期刊：
IEEE International Conference on Big Data (Big Data
影响因子：
0
作者：
Mangalagiri, Jayalakshmi;Sugumar, Jones Sam;Menon, Sumeet;Chapman, David;Yesha, Yaacov;Gangopadhyay, Aryya;Yesha, Yelena;Nguyen, Phuong
通讯作者：
Nguyen, Phuong

IDIOMS: Infectious Disease Imaging Outbreak Monitoring System

IDIOMS：传染病成像疫情监测系统

DOI：
10.1145/3428092
发表时间：
2021
期刊：
Digital Government: Research and Practice
影响因子：
0
作者：
Gangopadhyay, Aryya;Morris, Michael;Saboury, Babak;Siegel, Eliot;Yesha, Yelena
通讯作者：
Yesha, Yelena

Pairwise meta learning pipeline: classifying COVID-19 abnormalities on chest radio-graphs

成对元学习流程：对胸部 X 线照片上的 COVID-19 异常进行分类