CAREER: Systematic Mitigation of Deep Learning Adversaries in Medical Imaging

职业：系统地缓解医学成像领域的深度学习对手

• 批准号: 2046708
• 负责人: Pingkun Yan
• 金额: $ 54.96万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046708&HistoricalAwards=false
• 关键词: CAREER; Systematic; Mitigation; Deep; Learning

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.

随着大型医疗保健系统获得的大量数据，计算数据分析已成为处理和提取信息的医疗保健应用中的重要组成部分。深度学习是人工智能（AI）的子类别，由于其提取高级数据表示的强大能力，因此已将自己确立为数据分析的范式转移技术。但是，已知深度学习容易受到对手的影响，这会通过对输入数据样本做出很小的改变，从而导致算法产生截然不同的结果。在医学成像应用中，对手特别有害，在医学成像应用中，改变的图像可能导致AI算法引起医疗错误。因此，迫切需要创新和建立强大的医疗网络基础设施，以防止深度学习对手。该项目开发了新颖的AI技术，可以从系统的角度应对医学成像应用中对手的前所未有的挑战。它在医疗保健中实施AI并开发了缓解这些问题的新工具时，会引起人们对潜在问题的认识。这项研究将增强采用AI来提高医疗保健效率的信心，还将吸引和培训下一代AI研究人员和工程师。该项目旨在开发创新的AI技术，以系统地减轻医学成像应用中的深度学习对手。该项目是及时的，因为深度学习已被广​​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，必须解决一些挑战，包括对手的检测和纠正以及跨数据域的强大算法培训。现有方法仅与深度学习算法本身有关，并试图建立对任意对手的普遍盲目鲁棒性，该算法忽略了上游数据特征和下游任务细节。这项研究采用了一种整体方法，并围绕一系列集成的子主题进行组织，包括检测单个对抗性图像，将对抗性图像与不同来源区分，纠正对抗性图像，确定跨数据域稳健性的可传递性，以及量化输出不确定性。这项研究将提供新的见解，准确但强大的AI技术以及提高医学成像应用的鲁棒性的新策略。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响审查标准来通过评估来获得支持的。

项目成果

Connectome transformer with anatomically inspired attention for Parkinson's diagnosis

连接组变压器从解剖学角度激发帕金森氏症诊断的关注

• DOI: 10.1145/3535508.3545544
• 发表时间: 2022
• 期刊: Computational Biology and Health Informatics
• 作者: Machado-Reyes, Diego;Kim, Mansu;Chao, Hanqing;Shen, Li;Yan, Pingkun
• 通讯作者: Yan, Pingkun

Toward Adversarial Robustness in Unlabeled Target Domains

• DOI: 10.1109/tip.2023.3242141
• 发表时间: 2023-02
• 期刊: IEEE Transactions on Image Processing
• 影响因子: 10.6
• 作者: Jiajin Zhang;Hanqing Chao;Pingkun Yan

Revisiting the Trustworthiness of Saliency Methods in Radiology AI

• DOI: 10.1148/ryai.220221
• 发表时间: 2024-01-01
• 期刊: RADIOLOGY-ARTIFICIAL INTELLIGENCE
• 作者: Zhang, Jiajin;Chao, Hanqing;Yan, Pingkun
• 通讯作者: Yan, Pingkun