CAREER: Large Scale Learning for Complex Image-Omics Data Analytics

职业：复杂图像组学数据分析的大规模学习

• 批准号: 1553687
• 负责人: Junzhou Huang
• 金额: $ 53.58万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553687&HistoricalAwards=false
• 关键词: CAREER; Large; Scale; Learning; Complex

This proposal aims to develop computational tools for analyzing complex pathology and radiology image data as well genomics data. Recent technological innovations are enabling scientists to capture complex imaging and genomic data from different views. However, the major computational challenges are due to the unprecedented scale and complexity of heterogeneous data analytics. To solve the key and challenging problems in mining such comprehensive heterogeneous image and genomic data, the PI proposes to develop novel large scale learning tools and explore ways to integrate features from multiple data sources for clinical outcome prediction. It will greatly support the Precision Medicine Initiative, which has become a national goal and was unveiled by the U.S. government as a research effort designed to enable physicians to select individualized treatments. This project will facilitate the development of novel educational tools to enhance several current courses. The PI proposes an integrated research and education plan based on the following three components: (1) big image analytics and feature extraction, in which novel sparse convolution kernels, sparse deformable models and quantitative topology measurements are proposed to extract local and global features to fully characterize whole images; (2) large scale feature learning, in which domain knowledge guided sparse feature learning models and non-convex sparse feature learning models are proposed for large scale image marker discovery; and (3) multi-source image-omics data integration, in which sparse multi-view learning and large scale learning with bipartite graph are developed for big image-omics data integration, where the image-omics refers to both image data (pathology images or radiology images) and omics data (genomics, proteomics or metabolomics) captured from the same patient. This project will advance research in efficient feature learning from giga-pixel images, and in integrating heterogeneous image-omics data for outcome prediction and knowledge discovery. The success of this project will create a new paradigm for medical image informatics and big data.

该建议旨在开发用于分析复杂病理学和放射学图像数据以及基因组学数据的计算工具。最近的技术创新使科学家能够从不同观点捕获复杂的成像和基因组数据。但是，主要的计算挑战是由于异构数据分析的前所未有的规模和复杂性。为了解决挖掘如此全面的异质图像和基因组数据的关键和具有挑战性的问题，PI提议开发新型的大型学习工具，并探索从多个数据源中整合特征以进行临床结果预测的方法。它将极大地支持Precision Medicine Initiative，该计划已成为国家目标，并被美国政府作为一项研究工作，旨在使医师能够选择个性化治疗方法。该项目将促进开发新颖的教育工具，以增强当前课程。 PI基于以下三个组成部分提出了一项综合研究和教育计划：（1）大图像分析和特征提取，其中提出了新型稀疏卷积内核，稀疏可变形模型和定量拓扑测量，以提取局部和全球特征，以使整个图像充分表征整个图像； （2）大规模特征学习，其中为大规模图像标记发现了领域知识引导的稀疏特征学习模型和非凸线稀疏特征学习模型； （3）多源图像词数据集成，其中开发了用于大图像词的数据集成的稀疏多视图学习和大规模学习，其中图像词是指从同一患者捕获的图像数据（病理学图像或放射学图像）和摘要的图像数据（病理图像或放射学图像）和摘要数据（基因组学或蛋白质组学或蛋白质组学或转移）。该项目将从GIGA像素图像中提高有效特征学习的研究，并在整合异质图像词的数据中以进行结果预测和知识发现。该项目的成功将为医疗图像信息学和大数据创建一个新的范式。