Collaborative Research: SCH: Geometry and Topology for Interpretable and Reliable Deep Learning in Medical Imaging
合作研究:SCH:医学成像中可解释且可靠的深度学习的几何和拓扑
基本信息
- 批准号:2205417
- 负责人:
- 金额:$ 62.3万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-01 至 2026-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Deep learning models are being developed for safety-critical applications, such as health care, autonomous vehicles, and security. Their impressive performance has the potential to make profound impacts on human lives. For example, deep neural networks (DNNs) in medical imaging have been shown to have impressive diagnostic capabilities, often near that of expert radiologists. However, deep learning has not made it into standard clinical care, primarily due to a lack of understanding of why a model works and why it fails. The goal of this project is to develop methods for making machine learning models interpretable and reliable, and thus bridge the trust gap to make machine learning translatable to the clinic. This project achieves this goal through investigation of the mathematical foundations -- specifically the geometry and topology -- of DNNs. Based on these mathematical foundations, this project will develop computational tools that will improve the interpretability and reliability of DNNs. The methods developed in this project will be broadly applicable wherever deep learning is used, including health care, security, computer vision, natural language processing, etc.The power of a deep neural network lies in its hidden layers, where the network learns internal representations of input data. This research project centers around the hypothesis that geometry and topology provide critical tools for analyzing the internal representations of DNNs. The first goal of this project is to develop a rigorous mathematical and algorithmic foundation for describing the geometry and topology of a neural network's internal representations and then design efficient algorithms for geometric and topological computations necessary to explore these spaces. The next aim of this project is to apply these tools to improve the interpretability of deep learning. This will be done by linking a model's internal representation with interpretable and trusted features and by interactive visualization that explores the landscape of a model's internal representation. The next goal of this project focuses on model reliability, where geometry and topology will be used for failure identification, mitigation, and prevention. Finally, this project will test the developed techniques for reliable and interpretable neural networks in a real-world setting to aid expert oncologists in predicting patient outcomes in head and neck cancers, e.g., whether a tumor will metastasize.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.
正在为医疗保健、自动驾驶汽车和安全等安全关键应用开发深度学习模型。他们令人印象深刻的表现有可能对人类生活产生深远的影响。例如,医学成像中的深度神经网络 (DNN) 已被证明具有令人印象深刻的诊断能力,通常接近专家放射科医生的诊断能力。然而,深度学习尚未成为标准的临床护理,主要是由于缺乏对模型为何有效以及为何失败的理解。该项目的目标是开发使机器学习模型可解释且可靠的方法,从而弥合信任差距,使机器学习可转化为临床。该项目通过研究 DNN 的数学基础(特别是几何和拓扑)来实现这一目标。基于这些数学基础,该项目将开发计算工具,以提高 DNN 的可解释性和可靠性。该项目开发的方法将广泛适用于使用深度学习的任何地方,包括医疗保健、安全、计算机视觉、自然语言处理等。深度神经网络的强大之处在于其隐藏层,网络在隐藏层中学习输入数据的内部表示。该研究项目围绕这样一个假设:几何和拓扑为分析 DNN 的内部表示提供了关键工具。该项目的第一个目标是开发严格的数学和算法基础来描述神经网络内部表示的几何和拓扑,然后设计探索这些空间所需的几何和拓扑计算的有效算法。该项目的下一个目标是应用这些工具来提高深度学习的可解释性。这将通过将模型的内部表示与可解释和可信的特征联系起来,并通过交互式可视化来探索模型内部表示的景观来完成。该项目的下一个目标侧重于模型可靠性,其中几何和拓扑将用于故障识别、缓解和预防。最后,该项目将在现实环境中测试所开发的可靠且可解释的神经网络技术,以帮助肿瘤学家预测头颈癌患者的结果,例如肿瘤是否会转移。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,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Preston Fletcher其他文献
Preston Fletcher的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Preston Fletcher', 18)}}的其他基金
CAREER: Statistical Models and Classification of Time-Varying Shape
职业:时变形状的统计模型和分类
- 批准号:
1054057 - 财政年份:2011
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
相似国自然基金
Research on Quantum Field Theory without a Lagrangian Description
- 批准号:24ZR1403900
- 批准年份:2024
- 资助金额:0.0 万元
- 项目类别:省市级项目
Cell Research
- 批准号:31224802
- 批准年份:2012
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Cell Research
- 批准号:31024804
- 批准年份:2010
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Cell Research (细胞研究)
- 批准号:30824808
- 批准年份:2008
- 资助金额:24.0 万元
- 项目类别:专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
- 批准号:10774081
- 批准年份:2007
- 资助金额:45.0 万元
- 项目类别:面上项目
相似海外基金
Collaborative Research: SCH: Improving Older Adults' Mobility and Gait Ability in Real-World Ambulation with a Smart Robotic Ankle-Foot Orthosis
合作研究:SCH:使用智能机器人踝足矫形器提高老年人在现实世界中的活动能力和步态能力
- 批准号:
2306660 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
Collaborative Research: SCH: A wireless optoelectronic implant for closed-loop control of bi-hormone secretion from genetically modified islet organoid grafts
合作研究:SCH:一种无线光电植入物,用于闭环控制转基因胰岛类器官移植物的双激素分泌
- 批准号:
2306708 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
Collaborative Research: SCH: AI-driven RFID Sensing for Smart Health Applications
合作研究:SCH:面向智能健康应用的人工智能驱动的 RFID 传感
- 批准号:
2306790 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
Collaborative Research: SCH: Improving Older Adults' Mobility and Gait Ability in Real-World Ambulation with a Smart Robotic Ankle-Foot Orthosis
合作研究:SCH:使用智能机器人踝足矫形器提高老年人在现实世界中的活动能力和步态能力
- 批准号:
2306659 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
Collaborative Research: SCH: Therapeutic and Diagnostic System for Inflammatory Bowel Diseases: Integrating Data Science, Synthetic Biology, and Additive Manufacturing
合作研究:SCH:炎症性肠病的治疗和诊断系统:整合数据科学、合成生物学和增材制造
- 批准号:
2306740 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
Collaborative Research: SCH: Psychophysiological sensing to enhance mindfulness-based interventions for self-regulation of opioid cravings
合作研究:SCH:心理生理学传感,以增强基于正念的干预措施,以自我调节阿片类药物的渴望
- 批准号:
2320678 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
Collaborative Research: SCH: Therapeutic and Diagnostic System for Inflammatory Bowel Diseases: Integrating Data Science, Synthetic Biology, and Additive Manufacturing
合作研究:SCH:炎症性肠病的治疗和诊断系统:整合数据科学、合成生物学和增材制造
- 批准号:
2306738 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
Collaborative Research: SCH: AI-driven RFID Sensing for Smart Health Applications
合作研究:SCH:面向智能健康应用的人工智能驱动的 RFID 传感
- 批准号:
2306792 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
Collaborative Research: SCH: Therapeutic and Diagnostic System for Inflammatory Bowel Diseases: Integrating Data Science, Synthetic Biology, and Additive Manufacturing
合作研究:SCH:炎症性肠病的治疗和诊断系统:整合数据科学、合成生物学和增材制造
- 批准号:
2306739 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant
Collaborative Research: SCH: A wireless optoelectronic implant for closed-loop control of bi-hormone secretion from genetically modified islet organoid grafts
合作研究:SCH:一种无线光电植入物,用于闭环控制转基因胰岛类器官移植物的双激素分泌
- 批准号:
2306709 - 财政年份:2023
- 资助金额:
$ 62.3万 - 项目类别:
Standard Grant