Collaborative Research: SCH: Trustworthy and Explainable AI for Neurodegenerative Diseases

合作研究：SCH：值得信赖且可解释的人工智能治疗神经退行性疾病

• 批准号: 2123952
• 负责人: Eric Xing
• 金额: $ 36万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123952&HistoricalAwards=false
• 关键词: Collaborative; Research; SCH; Trustworthy; Explainable

Driven by its performance accuracy, machine learning (ML) has been used extensively for various applications in the healthcare domain. Despite its promising performance, researchers and the public have grown alarmed by two unsettling deficiencies of these otherwise useful and powerful models. First, there is a lack of trustworthiness - ML models are prone to interference or deception and exhibit erratic behaviors when in action dealing with unseen data, despite good practice during the training phase. Second, there is a lack of interpretability - ML models have been described as 'black-boxes' because there is little explanation for why the models make the predictions they do. This has called into question the applicability of ML to decision-making in critical scenarios such as image-based disease diagnostics or medical treatment recommendation. The ultimate goal of this project is to develop computational foundation for trustworthy and explainable Artificial Intelligence (AI), and offer a low-cost and non-invasive ML-based approach to early diagnosis of neurodegenerative diseases. In particular, the project aims to develop computational theories, ML algorithms, and prototype systems. The project includes developing principled solutions to trustworthy ML and making the ML prediction process transparent to end-users. The later will focus on explaining how and why an ML model makes such a prediction, while dissecting its underlying structure for deeper understanding. The proposed models are further extended to a multi-modal and spatial-temporal framework, an important aspect of applying ML models to healthcare. A verification framework with end-users is defined, which will further enhance the trustworthiness of the prototype systems. This project will benefit a variety of high-impact AI-based applications in terms of their explainability, trustworthy, and verifiability. It not only advances the research fronts of deep learning and AI, but also supports transformations in diagnosing neurodegenerative diseases. This project will develop the computational foundation for trustworthy and explainable AI with several innovations. First, the project will systematically study the trustworthiness of ML systems. This will be measured by novel metrics such as, adversarial robustness and semantic saliency, and will be carried out to establish the theoretical basis and practical limits of trustworthiness of ML algorithms. Second, the project provides a paradigm shift for explainable AI, explaining how and why a ML model makes its prediction, moving away from ad-hoc explanations (i.e. what features are important to the prediction). A proof-based approach, which probes all the hidden layers of a given model to identify critical layers and neurons involved in a prediction from a local point of view, will be devised. Third, a verification framework, where users can verify the model's performance and explanations with proofs, will be designed to further enhance the trustworthiness of the system. Finally, the project also advances the frontier of neurodegenerative diseases early diagnosis from multimodal imaging and longitudinal data by: (i) identifying retinal vasculature biomarkers using proof-based probing in biomarker graph networks; (ii) connecting biomarkers of the retina and the brain vasculature via cross- modality explainable AI model; and, (iii) recognizing the longitudinal trajectory of vasculature biomarkers via a spatio-temporal recurrent explainable model. This synergistic effort between computer science and medicine will enable a wide range of applications to trustworthy and explainable AI for healthcare. The results of this project will be assimilated into the courses and summer programs that the research team have developed with specially designed projects to train students with trustworthy and explainable AI.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.

受其性能准确性的推动，机器学习 (ML) 已广泛用于医疗保健领域的各种应用。尽管其性能令人鼓舞，但研究人员和公众对这些原本有用且强大的模型的两个令人不安的缺陷感到震惊。首先，缺乏可信度——尽管在训练阶段有良好的实践，但机器学习模型在处理看不见的数据时很容易受到干扰或欺骗，并表现出不稳定的行为。其次，缺乏可解释性——机器学习模型被描述为“黑匣子”，因为几乎没有解释为什么模型会做出这样的预测。这让人质疑机器学习在关键场景（例如基于图像的疾病诊断或医疗推荐）决策中的适用性。该项目的最终目标是为值得信赖和可解释的人工智能 (AI) 开发计算基础，并提供一种低成本、非侵入性的基于 ML 的方法来早期诊断神经退行性疾病。该项目尤其旨在开发计算理论、机器学习算法和原型系统。该项目包括开发可信赖的机器学习的原则性解决方案，并使机器学习预测过程对最终用户透明。后者将重点解释机器学习模型如何以及为何做出这样的预测，同时剖析其底层结构以加深理解。所提出的模型进一步扩展到多模态和时空框架，这是将机器学习模型应用于医疗保健的一个重要方面。定义了最终用户的验证框架，这将进一步增强原型系统的可信度。该项目将在可解释性、可信性和可验证性方面使各种高影响力的基于人工智能的应用程序受益。它不仅推进了深度学习和人工智能的研究前沿，而且支持神经退行性疾病诊断的转变。该项目将通过多项创新为可信赖且可解释的人工智能开发计算基础。首先，该项目将系统地研究机器学习系统的可信度。这将通过对抗性鲁棒性和语义显着性等新颖的指标来衡量，并将用于建立机器学习算法可信度的理论基础和实际限制。其次，该项目为可解释的人工智能提供了范式转变，解释了机器学习模型如何以及为何进行预测，摆脱了临时解释（即哪些特征对预测很重要）。将设计一种基于证明的方法，该方法探测给定模型的所有隐藏层，以从局部角度识别参与预测的关键层和神经元。第三，将设计一个验证框架，用户可以通过证明来验证模型的性能和解释，以进一步增强系统的可信度。最后，该项目还通过以下方式推进了多模态成像和纵向数据的神经退行性疾病早期诊断的前沿：（i）使用生物标记图网络中基于证据的探测来识别视网膜脉管系统生物标记； (ii) 通过跨模态可解释的人工智能模型连接视网膜和大脑脉管系统的生物标志物； (iii) 通过时空循环可解释模型识别脉管系统生物标志物的纵向轨迹。计算机科学和医学之间的这种协同努力将使可信赖且可解释的人工智能在医疗保健领域得到广泛应用。该项目的成果将被吸收到研究团队通过专门设计的项目开发的课程和暑期项目中，以培养具有值得信赖和可解释的人工智能的学生。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。