CAREER: Systematic Mitigation of Deep Learning Adversaries in Medical Imaging

随着大规模医疗保健系统获取大量数据，计算数据分析已成为医疗保健应用程序中处理和提取信息的重要组成部分。深度学习是人工智能（AI）的一个子类别，由于其强大的提取高级数据表示的能力，它已经成为数据分析的范式转移技术。然而，众所周知，深度学习容易受到对手的攻击，这会导致算法通过对输入数据样本进行非常小的更改来产生截然不同的结果。对手在医学成像应用中特别危险，因为改变的图像可能导致AI算法导致医疗错误。因此，迫切需要创新和建立强大的医疗网络基础设施，以防范深度学习对手。该项目开发新的人工智能技术，从系统的角度应对医学成像应用中对手前所未有的挑战。它在医疗保健领域实施人工智能时带来了对潜在问题的认识，并开发了新的工具来缓解这些问题。该研究将增强人们对采用人工智能提高医疗效率的信心，并将吸引和培训下一代人工智能研究人员和工程师。该项目旨在开发创新的人工智能技术，以系统地减轻医学成像应用中的深度学习对手。这个项目是及时的，因为深度学习已经广泛用于图像重建，质量增强，计算机辅助诊断以及图像引导的干预和手术。在实现强大的医学成像应用之前，必须解决几个挑战，包括对手的检测和纠正以及跨数据域的强大算法训练。现有的方法只关注深度学习算法本身，并试图建立针对任意对手的通用盲鲁棒性，这忽略了上游数据特征和下游任务细节。本研究采用整体方法，围绕一系列综合子主题进行组织，包括检测个体对抗图像，区分来自不同来源的对抗图像，纠正对抗图像，确定跨数据域的鲁棒性可转移性，以及量化输出不确定性。该研究将提供新的见解，准确而强大的人工智能技术，以及新的策略，以提高医学成像应用的鲁棒性。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。

With the enormous amounts of data being acquired by large-scale healthcare systems, computational data analysis has become an essential component in healthcare applications to process and extract information. Deep learning, a sub-category of artificial intelligence (AI), has established itself as a paradigm-shifting technology for data analytics due to its powerful ability to extract high-level data representations. However, deep learning is known to be vulnerable to adversaries, which cause algorithms to yield dramatically different results by making very small alterations to input data samples. Adversaries are particularly hazardous in medical imaging applications where an altered image may lead an AI algorithm to cause medical errors. Thus, there is an urgent need to innovate and build robust healthcare cyberinfrastructure to guard against deep learning adversaries. This project develops novel AI techniques to tackle the unprecedented challenges of adversaries in medical imaging applications from a systematic standpoint. It brings awareness to potential issues when implementing AI in healthcare and develops new tools to mitigate these issues. This research will bolster confidence in adopting AI to improve healthcare efficiency and will also attract and train the next generation of AI researchers and engineers.This project aims to develop innovative AI techniques to systematically mitigate deep learning adversaries in medical imaging applications. This project is timely as deep learning is already widely used in image reconstruction, quality enhancement, computer-aided diagnosis, and image-guided intervention and surgery. Several challenges, including detection and rectification of adversaries as well as robust algorithm training across data domains, must be resolved before achieving robust medical imaging applications. Existing methods are concerned with only the deep learning algorithms themselves and try to build universal blind robustness against arbitrary adversaries, which overlooks upstream data characteristics and downstream task specifics. This research adopts a holistic approach and is organized around a series of integrated subtopics, including detecting individual adversarial images, differentiating adversarial images from different sources, rectifying adversarial images, determining the transferability of robustness across data domains, and quantifying output uncertainties. The research will provide new insights, accurate yet robust AI techniques, and novel strategies to improve the robustness of medical imaging applications.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.