CIF: Small: Theory of Multiresolution Classification with Bases and Frames

CIF：小：基于基础和框架的多分辨率分类理论

• 批准号: 1017278
• 负责人: Jelena Kovacevic
• 金额: $ 41.1万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017278&HistoricalAwards=false
• 关键词: CIF; Small; Theory; Multiresolution; Classification

Recent advances in imaging of biological systems at all scales, from molecular and cellular up to organ levels, have given biologists and clinicians opportunities to observe processes and interactions at a never-before-seen level, leading to the collection of huge amounts of high-dimensional data. As a result, the visual inspection of these data sets,already error-prone, nonreproducible and subjective, has become impractical as well.There is thus an acute need for the development of systems to both automate this analysis, as well as mine interactions not visible to the human eye.The task of classification has been at the heart of several of the group's projects in the past few years, including the determination of developmental stages in fly embryos,the recognition of H&E-stained tissue types in stem-cell teratomas, and the diagnosis of otitis media. As an accurate and efficient algorithm for automated classification would have been of great use to biologists and clinicians, a multiresolution (MR) classificationalgorithm was developed and, in each of the problems, consistent trends emerged:1. MR classification always performed better than the no-MR version;2. Redundant MR transforms frames, always performed better than thenonredundant ones bases. This consistency across data sets and applications indicates that MR has the power to make a significant impact on biomedical image classification performance. The investigators thus study MR classification to gain fundamental understanding of its underpinnings, in particular, the following two questions:1. When/why does the MR classification work?2. When/why does the MR frame classification work?These questions are approached by setting up a measure-theoretic theory ofclassification as a mathematically rigorous framework within which to pose andinvestigate real-world classification problems.

从分子和细胞到器官水平到器官水平的各个尺度的生物系统成像的最新进展，使生物学家和临床医生有机会在从未见过的水平上观察过程和相互作用，从而收集了大量的高维数据。结果，对这些数据集的目视检查（已经容易出错，不可再生和主观）也变得不切实际。因此，急需开发系统以自动化这一分析的系统，以及人类眼中无法看到的地雷相互作用。在过去的几年中，范围内的范围内的范围是范围的范围。干细胞破坏瘤中的H＆E染色组织类型以及中耳炎的诊断。作为自动分类的准确有效算法，对生物学家和临床医生来说将有很大的用途，因此开发了多分辨率（MR）分类算法，并且在每种问题中都出现了一致的趋势：1。 MR分类的性能总是比NO-MR版本更好； 2。冗余MR变换帧总是比随后的基础更好。跨数据集和应用程序的这种一致性表明，MR有能力对生物医学图像分类性能产生重大影响。因此，研究人员研究了MR分类，以获得对其基础的基本理解，尤其是以下两个问题：1。何时/为什么MR分类工作？2。 MR框架分类何时起作用？通过设置一种测量理论的分类理论作为数学上严格的框架来解决这些问题？