图 书 馆
脑成像数据的无监督网络发现Unsupervised Network Discovery for Brain Imaging Data |
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| 课程网址: | http://videolectures.net/kdd2017_bai_network_discovery/ |
| 主讲教师: | Zilong Bai |
| 开课单位: | 加州大学 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  时空数据的一个常见问题是如何简化数据以发现一个基础网络，该网络由内聚的空间区域（节点）和这些区域之间的关系（边缘）组成。这种网络发现问题自然存在于众多领域中，包括气候数据（偶极子），天文数据（引力透镜）以及本文的重点（人类受试者的fMRI扫描）。鉴于先前的工作需要强有力的监督，我们提出了具有复杂约束和空间正则化的无监督矩阵三因子分解公式。我们表明，该公式在合成网络的受控实验中效果很好，并且能够恢复基本的地面真理网络。然后，我们表明，对于真实的功能磁共振成像数据，我们的方法可以在神经病学中再现关于静止状态下健康和阿尔茨海默氏症患者的默认模式网络的众所周知的结果。 |
| 课程简介: | A common problem with spatiotemporal data is how to simplify the data to discover an underlying network that consists of cohesive spatial regions (nodes) and relationships between those regions (edges). This network discovery problem naturally exists in a multitude of domains including climate data (dipoles), astronomical data (gravitational lensing) and the focus of this paper, fMRI scans of human subjects. Whereas previous work requires strong supervision, we propose an unsupervised matrix tri-factorization formulation with complex constraints and spatial regularization. We show that this formulation works well in controlled experiments with synthetic networks and is able to recover the underlying ground-truth network. We then show that for real fMRI data our approach can reproduce well known results in neurology regarding the default mode network in resting-state healthy and Alzheimer affected individuals. |
| 关 键 词: | 时空数据; 区域; 网络 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-14：nkq |
| 最后编审: | 2021-03-14：nkq |
| 阅读次数: | 43 |