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AF:Small: Novel Geometric Techniques for Several Biomedical Problems

AF:Small：解决多个生物医学问题的新颖几何技术

基本信息

批准号：
1716400
负责人：
Jinhui Xu
金额：
$ 45.18万
依托单位：
SUNY at Buffalo
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-15 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1716400&HistoricalAwards=false
关键词：
AF Small Novel Geometric Techniques

项目摘要

Recent progress in biomedicine has relied heavily on computer science technology. As the territory of biomedicine is rapidly enlarging, more powerful computational techniques are needed to foster its continuous growth. This project develops efficient computer algorithms for three fundamental geometric problems arising in several biomedical applications: (1) Truth Discovery, (2) Abnormal Clusters Detection, and (3) Resource Allocation Voronoi Diagram. Problem (1) develops quality-guaranteed polynomial time solutions to a key problem in data crowdsourcing, finding trustworthy information from multiple data sources, which is also motivated by the biomedical problem of learning critical information for improving treatment planning of endovascular intervention. Problem (2) detects extremely small-sized abnormal clusters from large datasets, which is motivated by detecting genomic structure variants from large populations. Problem (3) investigates new generalizations of the classical Voronoi diagram, which are motivated by a segmentation problem of biological images. This project will provide educational and research opportunities to undergraduate and graduate students (including those from under-represented groups), and develop a teaching evaluation tool for improving the quality of education. This project uses computational geometry techniques to develop novel algorithms for the proposed problems. It will introduce several general algorithmic techniques to the area of computational geometry, enriching and prodding its further development. These algorithmic techniques are also likely to be used in other areas, such as machine learning, computer vision, data mining, and pattern recognition, and bring new ideas to these areas. This project could lead to several long term impacts. It could potentially improve the quality of endovascular intervention, help identifying potential genomic structural variants in some genetic disorders, and provide more accurate quantitative information for medical image analysis.

生物医学的最新进展在很大程度上依赖于计算机科学技术。随着生物医学领域的迅速扩大，需要更强大的计算技术来促进其持续增长。这个项目为几个生物医学应用中出现的三个基本几何问题开发了有效的计算机算法：（1）真理发现，（2）异常集群检测，和（3）资源分配Voronoi图。问题（1）为数据众包中的一个关键问题开发了有质量保证的多项式时间解决方案，从多个数据源中找到值得信赖的信息，这也是出于学习关键信息以改善血管内介入治疗计划的生物医学问题。问题（2）从大数据集中检测极小尺寸的异常簇，其动机是从大群体中检测基因组结构变异。问题（3）研究了经典Voronoi图的新推广，其动机是生物图像的分割问题。该项目将为本科生和研究生（包括来自代表性不足群体的学生）提供教育和研究机会，并开发一种教学评估工具，以提高教育质量。这个项目使用计算几何技术来为所提出的问题开发新的算法。介绍了计算几何领域的几种常用算法技术，丰富和促进了计算几何的进一步发展。这些算法技术也有可能用于其他领域，如机器学习、计算机视觉、数据挖掘和模式识别，并为这些领域带来新的想法。该项目可能会产生一些长期影响。它可以潜在地提高血管内介入的质量，帮助识别某些遗传性疾病中潜在的基因组结构变异，并为医学图像分析提供更准确的定量信息。