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: 10391652
• 负责人: Xun Jia
• 金额: $ 65.83万
• 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/10391652
• 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; Human; Image; Image Analysis; Individual; Industrialization; Infrastructure; Institutes; 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; base; clinical application; clinical predictors; clinical translation; clinically relevant; deep learning; deep neural network; empowered; 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; research and development; 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.

基于对抗性的虚拟 CT 工作流程，用于评估医学影像中的人工智能 抽象的 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）， 一直是断层扫描研究、商业开发、临床转化、 最近，人们广泛认识到深度神经网络通常具有 普遍性问题，并且容易受到有意或无意的对抗性攻击，这一点至关重要。 必须解决挑战以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，以进一步加强对基于 AI/DL 的软件的监管 该计划强调的一项主要行动是“与医疗器械相关的监管科学方法”。 确保基于AI/DL的安全性和有效性的意义”。 SaMD 不能被高估，因为人工智能预计将在医学的未来中发挥关键作用。 在此背景下，学术界与 FDA 合作 R01 项目的总体目标是开展多样化的培训和 具有挑战性的低剂量 CT (LDCT) 扫描测试数据集，建立虚拟 CT 工作流程原型，并建立 基于人工智能的成像产品的评估方法，以支持 FDA 营销授权。 创新在于尖端的 DL 方法，该方法由 (a) 对抗性学习生成解剖学 以及人体胸部的病理学代表性特征；（b）对抗性攻击以探测虚拟 CT 各个步骤及其整体的工作流程；以及 (c) 系统评估方法，以更好地描述和评估 与其他 CT 模拟流程相比，我们的预测基于 AI 的成像产品的临床表现。 基于对抗性的 CT (ABC) 平台依靠对抗性学习来确保模型的多样性和真实性 模拟数据和图像并提高深度网络的泛化性，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能够强大地提供高 基于人工智能的成像研究和性能评估的质量数据集的具体目标是：（1）多样化。 患者建模 (SBU)、(2) 虚拟 CT 扫描 (UTSW)、(3) 深度 CT 成像 (RPI)、(4) 虚拟工作流程 验证（FDA），以及（5）ABC 系统传播（RPI-SBU-UTSW-FDA）。 对抗性学习将在生成临床语义特征方面发挥重要作用。 样本将以基于人工智能的互补方式在正弦图和图像领域生成。 不仅可以有效评估成像产品的准确性，还可以评估其普遍性和鲁棒性。 完成后，我们的 ABC 工作流程/平台将公开可用，并可轻松扩展到其他人 该 ABC 系统将通过 FDA 的目录共享。 监管科学工具，具有独特的优势，可以极大地促进开发、评估和 新兴的基于人工智能的成像产品的翻译。