Nobrainer: A robust and validated neural network tool suite for imagers

Nobrainer：适用于成像仪的强大且经过验证的神经网络工具套件

• 批准号: 10021957
• 负责人: Satrajit Sujit Ghosh
• 金额: $ 241.99万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10021957
• 关键词: Address; Adolescent; Architecture; Archives; Artificial Intelligence; BRAIN initiative; Bayesian neural network; Brain; Brain imaging; Brain scan; Cognitive; Collaborations; Collection; Communities; Computer software; Consumption; Data; Data Set; Data Sources; Development; Diagnosis; Disease; Educational workshop; Engineering; Ethics; Evaluation; Failure; Human; Image; Individual; Informatics; Institution; Label; Learning; Legal; Libraries; Longevity; Memory; Mental Health; Methods; Modeling; Nervous system structure; Network-based; Neural Network Simulation; Output; Pattern; Population; Population Heterogeneity; Privacy; Privatization; Process; Psychological Transfer; Pythons; Quality Control; Research; Research Personnel; Sampling; Scientist; Series; Software Engineering; Software Framework; Structure; Students; Technology; Testing; Three-Dimensional Imaging; Time; Training; Training Support; Trust; Uncertainty; Visualization; Work; algorithm training; base; citizen science; cognitive development; college; computerized data processing; connectome; data reuse; deep learning; distributed data; diverse data; high school; image processing; imager; improved; insight; large datasets; learning strategy; model development; network models; neural network; neuroimaging; neurophysiology; open source; software infrastructure; statistical learning; tool; user-friendly

There is an increasing need for efficient and robust software to process, integrate, and offer insight across the diversity of population imaging efforts underway across the BRAIN Initiative and other projects. Advances in statistical learning offer a set of technologies that can address many research applications using the extensive and varied data being produced by the projects. This can transform how we analyze and integrate new data. We propose using Nobrainer, an open source Python library that leverages these new learning technologies, as a platform that greatly simplifies integrating deep learning into neuroimaging research. Using this library, we are building and distributing user-friendly and cloud enabled end-user applications for the neuroimaging community. In Aim 1, we provide neural network models. We will create robust, pre-trained neural networks for brain segmentation and time series processing using brain scans from over 65000 individuals. Once trained, these models can then be used as the basis for many other applications, especially in reducing time of processing. We will subsequently use these base networks to perform image processing, image correction, and quality control. In Aim 2, we address the ability to train on private datasets. We will use Bayesian neural network models, which support principled use of prior information. We will use these networks to help detect when the models are expected to fail on an input, and provide visualizations to better understand how the model is working. In Aim 3, we focus on the engineering needed to maintain the software infrastructure, improve efficiency, and increase the scalability of our training methods. Here, we will extend, maintain, and disseminate Nobrainer, our open source software framework, together with training materials and ready to use, cloud-friendly, applications. We will also create much faster, neural network equivalents of time consuming image processing tasks (e.g., registration, segmentation, and annotation). The Nobrainer tools developed through these aims will allow users to find and apply the most pertinent applications and developers to extend the framework to support new architectures and disseminate new models and applications. We expect these tools to be used by any neuroimaging researcher through integration with BRAIN archives and popular software packages. These tools will significantly reduce data processing and new model development time, thus allowing faster exploration of hypotheses using public data and increase reusability of data through greater trust in model outputs.

越来越需要高效且强大的软件来处理，集成和提供洞察力 整个大脑计划和其他项目正在进行的人口成像工作的多样性。进步 统计学习提供了一系列技术，可以使用广泛的研究来解决许多研究应用 以及项目生产的各种数据。这可以改变我们分析和集成新数据的方式。我们 建议使用利用这些新学习技术的开源Python库Nobrainer作为一种 极大地简化了将深度学习整合到神经影像研究中的平台。使用此库，我们是 为神经影像建立和分发用户友好和云启用最终用户应用程序 社区。在AIM 1中，我们提供神经网络模型。我们将创建强大的，预先训练的神经网络 用于大脑分割和时间序列的处理，使用来自65000多名个体的脑扫描。一次 经过培训，这些模型然后可以用作许多其他应用的基础，尤其是在减少时间 加工。随后，我们将使用这些基本网络执行图像处理，图像校正和 质量控制。在AIM 2中，我们解决了在私人数据集上训练的能力。我们将使用贝叶斯神经 网络模型，支持先验信息的原则使用。我们将使用这些网络来帮助检测 当预期模型在输入上失败并提供可视化以更好地了解如何 模型正在起作用。在AIM 3中，我们专注于维护软件基础架构所需的工程， 提高效率，并提高我们的培训方法的可扩展性。在这里，我们将扩展，维护和 传播我们的开源软件框架Nobrainer，以及培训材料并准备使用， 云友好，应用程序。我们还将创建更快，更耗时的神经网络等效物 图像处理任务（例如，注册，细分和注释）。开发了Nobrainer工具 通过这些目标，用户可以找到并应用最相关的应用程序和开发人员来扩展 支持新体系结构并传播新模型和应用程序的框架。我们期望这些 通过与大脑档案和流行的整合，任何神经影像研究人员都可以使用的工具 软件包。这些工具将大大减少数据处理和新的模型开发时间，因此 使用公共数据允许更快地探索假设，并通过更大的 信任模型输出。

项目成果

TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models.

• DOI: 10.1038/s41592-022-01681-2
• 发表时间: 2022-12
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Ciric, Rastko;Thompson, William H. H.;Lorenz, Romy;Goncalves, Mathias;MacNicol, Eilidh E. E.;Markiewicz, Christopher J. J.;Halchenko, Yaroslav O. O.;Ghosh, Satrajit S. S.;Gorgolewski, Krzysztof J. J.;Poldrack, Russell A. A.;Esteban, Oscar
• 通讯作者: Esteban, Oscar

Privacy-preserving quality control of neuroimaging datasets in federated environments.

• DOI: 10.1002/hbm.25788
• 发表时间: 2022-05
• 期刊: Human brain mapping
• 影响因子: 4.8
• 作者: Saha DK;Calhoun VD;Du Y;Fu Z;Kwon SM;Sarwate AD;Panta SR;Plis SM
• 通讯作者: Plis SM

NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data.

• DOI: 10.1007/s12021-021-09525-8
• 发表时间: 2022-01
• 期刊: Neuroinformatics
• 影响因子: 3
• 作者: Senanayake N;Podschwadt R;Takabi D;Calhoun VD;Plis SM
• 通讯作者: Plis SM

GLACIER: GLASS-BOX TRANSFORMER FOR INTERPRETABLE DYNAMIC NEUROIMAGING.

Glacier：用于可解释动态神经成像的玻璃盒变压器。

• DOI: 10.1109/icassp49357.2023.10097126
• 发表时间: 2023
• 期刊: Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
• 作者: Mahmood,Usman;Fu,Zening;Calhoun,Vince;Plis,Sergey
• 通讯作者: Plis,Sergey