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: 2123809
• 负责人: My Thai
• 金额: $ 84万
• 依托单位: University of Florida
• 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=2123809&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.

由于其性能准确性，机器学习（ML）已广泛用于医疗保健领域的各种应用。尽管它的表现很有希望，但研究人员和公众对这些有用和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- ML模型容易受到干扰或欺骗，并且在处理看不见的数据时表现出不稳定的行为，尽管在训练阶段有良好的实践。其次，缺乏可解释性- ML模型被描述为“黑盒子”，因为几乎没有解释模型为什么会做出预测。这使得ML在关键场景中决策的适用性受到质疑，例如基于图像的疾病诊断或医疗建议。该项目的最终目标是为可信和可解释的人工智能（AI）开发计算基础，并为神经退行性疾病的早期诊断提供一种低成本和非侵入性的基于ML的方法。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ML的原则性解决方案，并使ML预测过程对最终用户透明。后者将专注于解释ML模型如何以及为什么做出这样的预测，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ML模型应用于医疗保健的一个重要方面。最终用户的验证框架的定义，这将进一步提高原型系统的可信度。该项目将使各种高影响力的基于AI的应用程序在可解释性，可信赖性和可验证性方面受益。它不仅推进了深度学习和人工智能的研究前沿，还支持诊断神经退行性疾病的转变。该项目将通过几项创新为值得信赖和可解释的AI开发计算基础。首先，该项目将系统地研究ML系统的可信度。这将通过对抗鲁棒性和语义显着性等新指标来衡量，并将用于建立ML算法可信度的理论基础和实际限制。其次，该项目为可解释的人工智能提供了一种范式转变，解释了ML模型如何以及为什么做出预测，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，用户可以用证据验证模型的性能和解释，以进一步提高系统的可信度。最后，该项目还通过以下方式推进了从多模态成像和纵向数据进行神经退行性疾病早期诊断的前沿：（i）在生物标志物图形网络中使用基于证据的探测来识别视网膜血管生物标志物;（ii）通过跨模态可解释的AI模型连接视网膜和脑血管的生物标志物;以及（iii）通过时空循环可解释模型识别脉管系统生物标记的纵向轨迹。计算机科学和医学之间的这种协同努力将使医疗保健领域的可靠和可解释的AI应用范围广泛。该项目的成果将被吸收到研究团队开发的课程和暑期课程中，这些课程和暑期课程通过专门设计的项目来培养学生具有值得信赖和可解释的AI。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进行评估而被认为值得支持。

项目成果

DOMINO: Domain-Aware Model Calibration in Medical Image Segmentation

• DOI: 10.1007/978-3-031-16443-9_44
• 发表时间: 2022-09
• 期刊: ArXiv
• 作者: Skylar E. Stolte;Kyle Volle;A. Indahlastari;Alejandro Albizu;A. Woods;Kevin Brink;Matthew Hale;R. Fang

XRand: Differentially Private Defense against Explanation-Guided Attacks

• DOI: 10.48550/arxiv.2212.04454
• 发表时间: 2022-12
• 作者: Truc D. T. Nguyen;Phung Lai;Nhathai Phan;M. Thai

An Explainer for Temporal Graph Neural Networks

• DOI: 10.1109/globecom48099.2022.10001619
• 发表时间: 2022-09
• 期刊: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
• 作者: Wenchong He;Minh N. Vu;Zhe Jiang;M. Thai

DOMINO: Domain-aware loss for deep learning calibration

• DOI: 10.1016/j.simpa.2023.100478
• 发表时间: 2023-02
• 期刊: Software impacts
• 作者: Skylar E. Stolte;Kyle Volle;A. Indahlastari;Alejandro Albizu;A. Woods;K. Brink;Matthew Hale;R. Fang

NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee

• DOI: 10.48550/arxiv.2209.08448
• 发表时间: 2022-09
• 期刊: ArXiv
• 作者: Minh N. Vu;Truc D. T. Nguyen;M. Thai