图 书 馆
脑成像的机器学习Machine learning for brain imaging |
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| 课程网址: | http://videolectures.net/mlpmsummerschool2014_varoquaux_brain_ima... |
| 主讲教师: | Gaël Varoquaux |
| 开课单位: | 因里亚萨克莱弗朗斯研究所 |
| 开课时间: | 2015-02-17 |
| 课程语种: | 英语 |
| 中文简介: |  在本次演讲中，我想展示一些使用脑成像来了解脑功能及其病理学时出现的机器学习问题的示例。首先，我将介绍大脑图像并推导统计特征。然后，我将讨论根据这些图像进行的预测如何用于诊断目的，以及如何作为了解大脑的窗口。我将重点介绍从脑图学习预测模型时出现的具体挑战，并详细介绍我们小组提出的解决方案，即空间惩罚。除了适当的统计图之外，我还将展示无监督建模和监督学习的组合如何通过自发性大脑活动来预测表型特征，而这种行为是在不控制受试者行为的情况下记录的。最后，我将详细说明我们的工作如何建立和滋养我们用来与从业人员互动的Python软件栈。 |
| 课程简介: | In this talk, I would like to showcase a few examples of machine learning problems that arise when using brain imaging to understand brain function and its pathologies. I'll first introduce brain images and to derive statistical features. Then I'll discuss how prediction from these images is useful for diagnostic purposes, but also as a windows to understand the brain. I'll highlight specific challenges that arise when learning predictive models from brain maps, and details solutions put forward by our group, namely spatial penalties. Moving beyond well-posed statistical maps, I'll show how a combination of unsupervised modeling and supervised learning can predict phenotypic traits from spontaneous brain activity, recorded without controlling the subjects behavior. Finally, I'll detail how our work builds upon and nourishes a Python software stack that we leverage to interact with practitioners. |
| 关 键 词: | 脑成像; 预测表型; Python |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-10-29：zyk |
| 最后编审: | 2020-10-29：zyk |
| 阅读次数: | 50 |