Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging

基于对抗性的虚拟 CT 工作流程，用于评估医学影像中的人工智能

• 批准号: 10592427
• 负责人: Xun Jia
• 金额: $ 61.56万
• 依托单位: RENSSELAER POLYTECHNIC INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10592427
• 关键词: Acceleration; Address; Agreement; Anatomy; Artificial Intelligence; Authorization documentation; Big Data; Bypass; Catalogs; Chest; Classification; Clinical; Code; Communities; Computer software; Data; Data Set; Detection; Development; Diagnostic; Disease; Effectiveness; Elements; Ensure; Evaluation; Evaluation Methodology; Feedback; Future; Generations; Goals; Head; Human; Image; Image Analysis; Individual; Industrialization; Infrastructure; Internet; Label; Learning; Machine Learning; Mainstreaming; Marketing; Masks; Medical; Medical Device; Medical Imaging; Medicine; Methods; Modeling; Nature; Pathologic; Pathway Analysis; Patients; Performance; Play; Population; Positioning Attribute; Publishing; Regulation; Research; Research Personnel; Retrieval; Role; Running; Safety; Sampling; Science; Semantics; System; Techniques; Testing; Texas; Training; Translations; Universities; Validation; Vendor; Work; X-Ray Computed Tomography; algorithmic bias; authority; clinical application; clinical predictors; clinical translation; clinically relevant; deep learning; deep neural network; empowerment; federated learning; image processing; image reconstruction; imaging modality; improved; innovation; interest; learning strategy; low dose computed tomography; lung cancer screening; novel strategies; patient privacy; prototype; radiomics; reconstruction; simulation; tomography; tool; virtual; virtual patient; web site

Adversarially Based Virtual CT Workflow for Evaluation of AI in Medical Imaging ABSTRACT Over the past several years, artificial intelligence (AI) and machine learning (ML), especially deep learning (DL), has been the most prominent direction of tomographic research, commercial development, clinical translation, and FDA evaluation. Recently, it has become widely recognized that deep neural networks often have generalizability issues and are vulnerable to adversarial attacks, deliberate or unintentional. This critical challenge must be addressed to optimize the performance of deep neural networks in medical applications. In January this year, FDA published an action plan for furthering the oversight for AI/DL-based software as medical devices (SaMDs). One major action underlined in the plan is “regulatory science methods related to algorithm bias and robustness”. The significance of ensuring the safety and effectiveness of AI/DL-based SaMDs cannot be overestimated since AI is expected to play a critical role in the future of medicine. In this context, the overall goal of this academic-FDA partnership R01 project is to generate diverse training and challenging testing datasets of low-dose CT (LDCT) scans, prototype a virtual CT workflow, and establish an evaluation methodology for AI-based imaging products to support FDA marketing authorization. The technical innovation lies in cutting-edge DL methods empowered by (a) adversarial learning to generate anatomically and pathologically representative features in the human chest; (b) adversarial attacking to probe the virtual CT workflow in individual steps and its entirety; and (c) systematic evaluation methods to better characterize and predict the clinical performance of AI-based imaging products. In contrast to other CT simulation pipelines, our Adversarially Based CT (ABC) platform relies on adversarial learning to ensure diversity and realism of the simulated data and images and improve the generalizability of deep networks, and utilizes adversarial samples to probe the ABC workflow to address the robustness of deep networks. The overarching hypothesis is that adversarial learning and attacking methods are powerful to deliver high- quality datasets for AI-based imaging research and performance evaluation. The specific aims are: (1) diverse patient modeling (SBU), (2) virtual CT scanning (UTSW), (3) deep CT imaging (RPI), (4) virtual workflow validation (FDA), and (5) ABC system dissemination (RPI-SBU-UTSW-FDA). In this project, generative adversarial learning will play an instrumental role in generating features of clinical semantics. Also, adversarial samples will be produced in both sinogram and image domains. In these complementary ways, AI-based imaging products can be efficiently evaluated for not only accuracy but also generalizability and robustness. Upon completion, our ABC workflow/platform will be made publicly available and readily extendable to other imaging modalities and other diseases. This ABC system will be shared through the FDA’s Catalog of Regulatory Science Tools, and uniquely well positioned to greatly facilitate the development, assessment and translation of emerging AI-based imaging products.

基于对抗机制的虚拟CT工作流程在医学影像AI评价中的应用 摘要 在过去的几年里，人工智能（AI）和机器学习（ML），特别是深度学习（DL）， 一直是断层摄影研究、商业开发、临床翻译、 FDA评估。最近，人们已经广泛认识到，深度神经网络通常具有 普遍性问题，并且容易受到蓄意或无意的对抗性攻击。这一关键 必须解决的挑战是优化深度神经网络在医疗应用中的性能。 今年1月，FDA发布了一项行动计划，进一步监督基于AI/DL的软件， 医疗器械（SaMD）。该计划中强调的一项主要行动是"与以下方面有关的监管科学方法： 算法偏差和鲁棒性"。确保基于AI/DL的安全性和有效性的重要性 SaMD不能被高估，因为人工智能预计将在未来的医学中发挥关键作用。在这 在此背景下，该学术-FDA合作伙伴关系R01项目的总体目标是开展多样化的培训， 挑战低剂量CT（LDCT）扫描的测试数据集，建立虚拟CT工作流程原型，并建立 基于AI的成像产品的评估方法，以支持FDA的上市许可。技术 创新在于尖端的DL方法，这些方法由（a）对抗性学习来生成解剖学上的 和人类胸部中的病理代表性特征;（B）对抗性攻击以探测虚拟CT （c）系统评价方法，以更好地描述和 预测基于AI的成像产品的临床性能。与其他CT仿真管道相比，我们的 基于对抗的CT（ABC）平台依赖于对抗学习来确保 模拟数据和图像，提高深度网络的泛化能力，并利用对抗性样本 探索ABC工作流程，以解决深度网络的鲁棒性问题。 总体假设是，对抗性学习和攻击方法是强大的，可以提供高- 用于基于AI的成像研究和性能评估的高质量数据集。具体目标是：（1）多样化 患者建模（SBU），（2）虚拟CT扫描（UTSW），（3）深部CT成像（RPI），（4）虚拟工作流程 验证（FDA）和（5）ABC系统传播（RPI-SBU-UTSW-FDA）。在这个项目中， 对抗性学习将在生成临床语义学特征方面发挥重要作用。此外，adversarial 将在正弦图和图像域中产生样本。通过这些互补的方式，基于AI的 成像产品不仅可以有效地评估准确性，还可以有效地评估通用性和鲁棒性。 完成后，我们的ABC工作流程/平台将公开提供，并随时可扩展到其他 成像模式和其他疾病。该ABC系统将通过FDA的 监管科学工具，并具有独特的优势，大大促进了开发，评估和 翻译新兴的基于AI的成像产品。