NSF Convergence Accelerator Track D: Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for Accelerated Impact (STRAIT I3)

NSF 融合加速器轨道 D：可扩展、可追踪的成像翻译人工智能：加速影响的创新实施 (STRAIT I3)

• 批准号: 2040462
• 负责人: Bennett Landman
• 金额: $ 99.95万
• 依托单位: Vanderbilt University
• 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: 2020-09-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040462&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Scalable

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This project, Convergence Accelerator Track D: Scalable, TRaceable Ai for Imaging Translation: Innovation to Implementation for accelerated Impact (STRAIT I3), addresses fundamental gaps between the science and the engineering that is preventing the effective use of AI models with medical imaging data. The project will leverage the large number of open dataset efforts available for medical imaging, including imaging resources for COVID-19. Thousands of AI models are also published in the scientific literature each year for such data. Yet, these resources are not consistently accessible at scale nor are they able to be validated for clinical application. This project includes three thrust areas to address this problem. Thrust Area 1 democratizes access to data sets through traceable data annotation. Thrust Area 2 transforms the assessment and peer review process for data, to ensure fair and consistent evaluation of technologies. Thrust Area 3 targets reproducible execution and comparison of models to facilitate translation to practice. In Phase I, this Convergence Accelerator project will create direct public health and technology benefits by enhancing the radiological assessment of COVID-19 pneumonia. In Phase II, it will extend these benefits into a medical imaging ecosystem spanning multiple medical imaging domains. Broader impacts will be achieved by engaging various identified communities through professionally led studios, consented A/B testing studies, and structured outreach. All project thrusts utilized open software and commodity hardware, wherever possible, so that the innovations from this project on scalable image data validation will enhance other related efforts in open source software, open science, reproducible science, and findable science.This project works towards achieving a fundamental rethinking in how model-centric AI could be validated and translated in medical imaging, algorithm design, and medical science. The intellectual activities are organized around three research thrusts, each addressing an essential challenge that currently confronts the development and translation of AI-based medical imaging tools. One research thrust is on creating a lightweight data provenance and annotation interface compatible with both clinical imaging and research studies. The second is on facilitating rapid innovation in AI architectures while creating an enhanced validation/peer review process to avoid irreproducible implementations and overtraining of models. The third thrust is the integration of these efforts into a novel Model Zoo to provide robust capabilities for validation, assessment, and translation. This research effort will utilize core scientific innovations from the collaborative team consisting of members from a university (Vanderbilt), a medical center (Vanderbilt Medical Center), two industry partners (MD.ai, Kaggle), and a professional society (SIIM), alongside widely used, open source platforms. In Phase 1 of the Convergence Accelerator, the project will focus on newly created public and private datasets for COVID-19. Phase II will scale this approach to different medical imaging modalities, including dermatology and ophthalmology.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.

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，以应对国家重要性的挑战，并将在不久的将来为社会提供有价值的成果。这个项目，融合加速器轨道D：可扩展的，可跟踪的AI成像翻译：创新到实施加速影响（STRAIT I3），解决了科学和工程之间的根本差距，阻碍了AI模型与医学成像数据的有效使用。该项目将利用大量可用于医学成像的开放数据集，包括COVID-19的成像资源。每年，科学文献中也会针对这些数据发布数千个人工智能模型。然而，这些资源在规模上并不一致，也不能被验证用于临床应用。该项目包括解决这一问题的三个重点领域。Thrust Area 1通过可追踪的数据注释使数据集的访问民主化。Thrust Area 2转变了数据的评估和同行评审流程，以确保对技术进行公平和一致的评估。Thrust Area 3的目标是可重复的执行和模型比较，以促进转化为实践。在第一阶段，这个融合加速器项目将通过加强对COVID-19肺炎的放射评估，创造直接的公共卫生和技术效益。在第二阶段，它将把这些好处扩展到一个跨越多个医学成像领域的医学成像生态系统中。通过专业领导的工作室、经同意的A/B测试研究和结构化的外联活动，使各种确定的社区参与进来，将产生更广泛的影响。所有项目都尽可能利用开放软件和商用硬件，因此该项目在可扩展图像数据验证方面的创新将增强开源软件、开放科学、可重复科学和可发现科学方面的其他相关工作。该项目致力于从根本上重新思考如何在医学成像、算法设计和医学科学中验证和转化以模型为中心的人工智能。智力活动围绕三个研究方向组织，每个方向都解决了目前基于人工智能的医学成像工具的开发和翻译所面临的一个基本挑战。一个研究重点是创建一个轻量级的数据出处和注释接口兼容的临床成像和研究。第二个是促进人工智能架构的快速创新，同时创建增强的验证/同行评审流程，以避免不可复制的实现和模型的过度训练。第三个重点是将这些努力整合到一个新的模型动物园中，以提供强大的验证，评估和翻译功能。这项研究工作将利用由一所大学（范德比尔特），一个医疗中心（范德比尔特医疗中心），两个行业合作伙伴（MD.ai，Kaggle）和一个专业协会（SIIM）成员组成的合作团队的核心科学创新，以及广泛使用的开源平台。在融合加速器的第一阶段，该项目将专注于新创建的COVID-19公共和私人数据集。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

Exploring shared memory architectures for end-to-end gigapixel deep learning

探索端到端千兆像素深度学习的共享内存架构

• 发表时间: 2023
• 期刊: Medical Imaging with Deep Learning (MIDL
• 作者: Lucas W. Remedios, Leon Y.

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

• DOI: 10.48550/arxiv.2304.04155
• 发表时间: 2023-04
• 期刊: ArXiv
• 作者: Ruining Deng;C. Cui;Quan Liu;Tianyuan Yao;Lucas W. Remedios;Shunxing Bao;Bennett A. Landman;L. Wheless;Lori A. Coburn;K. Wilson;Yaohong Wang;Shilin Zhao;A. Fogo;Haichun Yang;Yucheng Tang;Yuankai Huo

Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis

通过随机游走滑动窗口减轻高分辨率组织学全切片图像合成中的平铺效应

• 发表时间: 2023
• 期刊: Medical Imaging with Deep Learning
• 作者: Shunxing Bao, Ho Hin

All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning

• DOI: 10.1088/1742-6596/2722/1/012012
• 发表时间: 2023-07
• 期刊: Journal of Physics: Conference Series
• 作者: C. Cui;Ruining Deng;Quan Liu;Tianyuan Yao;Shunxing Bao;Lucas W. Remedios;Yucheng Tang;Yuankai Hu

Label efficient segmentation of single slice thigh CT with two-stage pseudo labels.

使用两阶段伪标签对单层大腿 CT 进行标签有效分割。

• DOI: 10.1117/1.jmi.9.5.052405
• 发表时间: 2022
• 期刊: Journal of medical imaging (Bellingham, Wash.)
• 作者: Yang,Qi;Yu,Xin;Lee,HoHin;Tang,Yucheng;Bao,Shunxing;Gravenstein,KristoferS;Moore,AnnZenobia;Makrogiannis,Sokratis;Ferrucci,Luigi;Landman,BennettA
• 通讯作者: Landman,BennettA