Protected Radiomics Analysis Commons for Deep Learning in Biomedical Discovery

生物医学发现中深度学习的受保护放射组学分析共享

• 批准号: 9494294
• 负责人: Maryellen L. Giger
• 金额: $ 33.89万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-18 至 2019-09-17
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9494294
• 关键词: Algorithmic Analysis; Algorithms; Area; Characteristics; Complex; Computer Systems; Computer software; Computers; Data; Development; Dimensions; Environment; Funding; Health Insurance Portability and Accountability Act; High Performance Computing; Image; Image Analysis; Machine Learning; Medical Imaging; Memory; Productivity; Protocols documentation; Request for Proposals; Research; Research Infrastructure; Research Personnel; Schedule; Secure; Services; System; Techniques; Translating; United States National Institutes of Health; biological systems; cluster computing; computer cluster; deep learning; genomic data; image reconstruction; imaging system; phenotypic data; quantitative imaging; radiomics; reconstruction; tomography; tumor; virtual

Abstract Development of emerging imaging systems for biological and medical imaging often involves multi-dimensional acquisition protocols and thus correspondingly the need for complex reconstruction algorithms and advanced machine learning algorithms in order to produce the desired resulting image and quantitative tumor characteristics. This proposal requests funding for a high performance computing system, entitled Protected Radiomics Analysis Commons for Deep Learning in Biomedical Discovery, to further the development and application of deep learning techniques to quantitative image analysis and image reconstruction. There are 12 specific NIH projects that will benefit from the proposed computing infrastructure system. We present the 12 projects through examples from within four Specific Research Topics areas: (a) tomographic image reconstruction, (b) quantitative image analysis, (c) functional quantification, and (d) association of radiomics (image data) with phenotypic and genomic data. The proposed system is a computing cluster, which uses ScaleMP's Versatile SMP software to aggregate the cluster nodes into a single symmetric multiprocessing computer. The major hardware components consist of 1 HP Enterpris\e ProLiant DL380 server and 8 Apollo 6500 compute nodes, with a total of 2.1 TB of main memory, 18 Intel Xeon E5-2640v4 10-core CPUs, and 32 nVidia Tesla P100 GPUs. The servers will be connected via a 100Gbps EDR Infiniband network. In addition, three important software components, which aim to reduce the complexity of the computing environment and increase researcher productivity, will be integrated into the hardware components: the aforementioned ScaleMP vSMP to create a single virtual computer from the cluster nodes, Cendio ThinLinc to provide remote desktop graphical login services, and Bitfusion Flex AI Platform which provides GPU virtualization, scheduling, and optimization, as well as curated container deployment of common deep learning frameworks. The Protected Radiomics Analysis Commons for Deep Learning in Biomedical Discovery will allow researchers to expedite, extend, and translate their current NIH funding in areas of many-dimensional image reconstruction and image analysis algorithms – all of which will benefit greatly from deep learning approaches – within a secure, protected, and HIPAA- compliant sharable environment.

抽象的 开发用于生物学和医学成像的新兴成像系统通常涉及 多维采集方案，因此需要复杂重建 为了产生所需的图像，算法和高级机器学习算法 和定量肿瘤特征。该建议要求资助高性能 计算系统，有资格的受保护的放射线分析共享，用于生物医学深度学习 发现，进一步开发和应用深度学习技术来定量 图像分析和图像重建。有12个特定的NIH项目将从 建议的计算基础架构系统。我们通过内部示例介绍了12个项目 四个特定的研究主题领域：（a）层析成像图像重建，（b）定量图像 分析，（c）功能定量和（d）放射线学（图像数据）与表型的关联 和基因组数据。提出的系统是一个计算集群，它使用Scalemp的多功能 SMP软件将群集节点汇总到单个对称的多处理计算机中。这 主要硬件组件由1 HP Enterprises \ E Proliant DL380服务器和8 Apollo 6500组成 计算节点，总内存总数为2.1 TB，18英特尔Xeon E5-2640V4 10核CPU和 32 NVIDIA TESLA P100 GPU。服务器将通过100Gbps EDR Infiniband网络连接。在 此外，三个重要的软件组件，旨在降低计算的复杂性 环境和提高研究人员的生产率将集成到硬件组件中： Forement Intuned Scalemp VSMP从群集节点创建单个虚拟计算机，Cendio 提供远程桌面图形登录服务和BitFusion Flex AI平台 提供GPU虚拟化，调度和优化，以及策划的容器部署 常见的深度学习框架。深度学习的受保护的放射组学分析共享 在生物医学发现中，研究人员可以加快，扩展和翻译其当前的NIH 多维图像重建和图像分析算法的资金 - 所有 这将从深度学习的方法中受益匪浅 - 在安全，受保护和HIPAA中 合规性环境。

项目成果

Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19.

• DOI: 10.1117/1.jmi.8.s1.014503
• 发表时间: 2021-01
• 期刊: Journal of medical imaging (Bellingham, Wash.)
• 作者: Hu Q;Drukker K;Giger ML
• 通讯作者: Giger ML