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

基于对抗性的医学影像人工智能评估虚拟CT工作流 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)， 一直是断层成像研究、商业开发、临床翻译的最突出方向， 以及FDA的评估。最近，人们普遍认识到，深度神经网络通常具有 概括性问题，容易受到有意或无意的敌意攻击。这一点至关重要 必须解决的挑战是优化医学应用中的深度神经网络的性能。 今年1月，FDA发布了一项行动计划，以进一步监督基于AI/DL的软件，如 医疗器械(SAMD)。该计划强调的一项主要行动是“与以下方面有关的监管科学方法 算法偏差和健壮性“。保障人工智能/数字图书馆安全性和有效性的意义 由于人工智能预计将在未来的医学中发挥关键作用，因此SAMD怎么估计都不为过。在这 背景下，这一学术-FDA伙伴关系R01项目的总体目标是产生多样化的培训和 挑战低剂量CT(LDCT)扫描的测试数据集，构建虚拟CT工作流程的原型，并建立 基于人工智能的成像产品的评估方法，以支持FDA的营销授权。技术上的 创新在于通过(A)对抗性学习在解剖学上生成的尖端DL方法 (B)对抗性攻击探查虚拟CT 个别步骤的工作流程及其整体；和(C)系统的评价方法，以更好地描述和 预测基于人工智能的影像产品的临床性能。与其他CT仿真管道相比，我们的 基于对抗性的CT(ABC)平台依靠对抗性学习来确保数据的多样性和真实性 模拟数据和图像，提高了深度网络的泛化能力，并利用对抗性样本 探索ABC工作流，以解决深层网络的健壮性。 最重要的假设是，对抗性学习和攻击方法是强大的，以提供高 用于基于人工智能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)深度CT成像(RPI)、(4)虚拟工作流 验证(FDA)，以及(5)ABC系统传播(RPI-SBU-UTSW-FDA)。在这个项目中，生成性 对抗性学习将在产生临床语义学特征方面发挥工具作用。还有，对抗性的 样本将在正弦图域和图像域产生。在这些互补的方式中，基于人工智能的 成像产品不仅可以从准确性、可推广性和稳健性方面进行有效的评估。 完成后，我们的ABC工作流程/平台将公开可用，并可随时扩展到其他 成像方式和其他疾病。该ABC系统将通过FDA的目录共享 监管科学工具，并处于独特的有利地位，极大地促进了开发、评估和 翻译新兴的基于人工智能的成像产品。

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.