CRI: IAD: Acquisition of Research Infrastructure for Knowledge-enhanced, Large-scale Learning of Multimodality Visual Data

与视觉数据获取技术的发展和获取的数据集的爆炸性收集相比，从非常大的、多样的、不同种类的视觉数据集中进行知识发现和学习的计算技术仅仅是适度的发展，最终阻碍了更有效的利用和更好的理解。该项目旨在弥合上述差距，并在几何引导的多模式视觉数据知识发现方面培养一个强大的研究计划，重点是神经成像应用。具体地说，该项目的重点是：(1)探索基于黎曼几何的计算三维流形几何结构的新工具，并开发一种新的具有几何流的体映射；(2)为半监督数据聚类建立严格的数学基础；以及(3)将几何映射和半监督学习的方法扩展到(高阶)异构体视觉数据分析。一旦开发出来，这些新的算法就被应用于各种重要脑部疾病的计算机辅助诊断，如肿瘤和脑功能障碍。该项目可以帮助确定人脑中的疾病模式，从而可能为很大一部分人口提供临床和社会效益。此外，该项目可以立即帮助将现有资源和正在进行的研究提升到统一、系统的水平，并加强计算机科学教育。研究结果将通过软件工具(包括源代码)的免费网络访问和通过项目网站(http://www.cs.wayne.edu/~mdong/NSF_CRI.html).的样本数据集(包括原始神经成像数据和处理后的数据，如高分辨率脑表面网格)向计算机科学和医学界广泛传播

Compared to the development of visual data acquisition technology and the explosive collection of acquired datasets, computational techniques for knowledge discovery and learning from very large, diverse, heterogeneous visual datasets have only evolved modestly, ultimately impeding the more effective utilization and better understanding. The project aims to bridge the aforementioned gaps and foster a strong research program in geometry-guided knowledge discovery in multimodality visual data, with an emphasis on neuroimaging applications. Specifically, the project focuses on: (1) exploring new tools based on Riemannian geometry for computing geometric structures of 3-manifolds and developing a novel volumetric mapping with geometric flow; (2) developing a rigorous mathematical foundation for semi-supervised data clustering; and (3) extending our approaches on geometric mapping and semi-supervised learning to (high-order) heterogeneous volumetric visual data analysis. Once developed, these novel algorithms are applied to computer assisted diagnosis of various important brain diseases, such as tumors and brain functional disorder. This project can help with identifying disease patterns in human brain, and thus possibly provides both clinical and social benefits to a large sector of the population. Moreover, the project can immediately help to elevate the existing resources and on-going research to a unified, systematic level and strengthen computer science education. The research results will be widely disseminated to both computer science and medical communities through free Web access of the software tools (including source codes), and the set of sample data (including raw neuroimaging data and processed ones such as high-resolution brain surface meshes) via the project Web site (http://www.cs.wayne.edu/~mdong/NSF_CRI.html).