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; 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.

在其性能准确性的驱动下，机器学习（ML）已被广泛用于医疗领域的各种应用。尽管表现出色，但研究人员和公众对这些原本有用且强大的模型的两种令人不安的缺陷感到震惊。首先，缺乏可信赖性 - ML模型容易受到干扰或欺骗，尽管在训练阶段进行了良好的练习，但在处理看不见数据的行动时表现出不稳定的行为。其次，缺乏可解释性-ML模型被描述为“黑盒”，因为对于为什么模型做出预测的原因很少。这使ML在基于图像的疾病诊断或医疗建议等关键情况下对决策的适用性提出了质疑。该项目的最终目的是为可信赖和可解释的人工智能（AI）建立计算基础，并提供低成本和非侵入性ML的方法来早期诊断神经退行性疾病。特别是，该项目旨在开发计算理论，ML算法和原型系统。该项目包括开发可信赖的ML的原则解决方案，并使ML预测过程透明到最终用户。后来的将重点放在解释ML模型如何以及为何进行这样的预测，同时剖析其潜在结构以深入理解。提出的模型进一步扩展到了多模式和时空框架，这是将ML模型应用于医疗保健的重要方面。定义了具有最终用户的验证框架，这将进一步增强原型系统的可信度。该项目将在其解释性，可信赖和可验证性方面受益于各种高影响AI的应用程序。它不仅可以提高深度学习和AI的研究方面，而且还支持诊断神经退行性疾病的转变。该项目将为可信赖且可解释的AI提供一些创新的计算基础。首先，该项目将系统地研究ML系统的可信度。这将通过新颖的指标（例如对抗性鲁棒性和语义显着性）来衡量，并将进行以建立ML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AI提供了范式转变，并解释了ML模型如何以及为什么进行预测，而不是临时解释（即，哪些功能对预测很重要）。从当地的角度来探讨给定模型的所有隐藏层，从当地的角度识别参与预测的关键层和神经元的所有隐藏层。第三，用户可以在其中验证模型的性能和证明的解释，旨在进一步增强系统的可信度。最后，该项目还通过以下方式从多模式成像和纵向数据中提高了神经退行性疾病的前沿，并通过以下方式诊断出：（i）使用生物标志物图网络中的基于证明的探测器识别视网膜脉管体生物标志物； （ii）通过可解释的AI模型连接视网膜和脑脉管系统的生物标志物； （iii）通过时空复发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协同工作将使广泛的应用程序可以在医疗保健中值得信赖和可解释的AI。该项目的结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夏季课程中，以培训具有值得信赖和可解释的AI的学生。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。