像素之外的生命:HCS的机器学习和图像分析方法Life beyond the pixels: machine learning and image analysis methods for HCS |
|
课程网址: | http://videolectures.net/icgeb_horvath_image_analysis_methods/ |
主讲教师: | Peter Horvath |
开课单位: | 匈牙利科学院 |
开课时间: | 2019-06-28 |
课程语种: | 英语 |
中文简介: | 在本次演讲中,我将概述基于单个单元格的高内容屏幕分析中的计算步骤。首先,我将介绍一种新颖的显微图像校正方法,该方法旨在消除渐晕和不均匀背景效应,这会导致未经校正的、基于强度的测量结果被破坏。我将讨论高级细胞分类器(Advanced Cell Classifier,ACC)([url][url][url].cellclassifier.org),这是一种能够根据从图像中提取的特征识别细胞表型的软件工具。它为用户提供了一个界面,可以有效地训练机器学习方法来预测各种表型。我们开发了Suggest a Learner(SALT)工具箱,用于为特定分类问题选择最佳机器学习算法和参数。对于基于离散细胞的决策不适用的情况,我们提出了一种使用多参数回归分析连续生物现象的方法。最后,为了提高学习速度和准确性,我们最近开发了一种主动学习方案,它可以自动选择信息量最大的单元样本。 |
课程简介: | In this talk I will give an overview of the computational steps in the analysis of a single cell-based high-content screen. First, I will present a novel microscopic image correction method designed to eliminate vignetting and uneven background effects which, left uncorrected, corrupt intensity-based measurements. I will discuss the Advanced Cell Classifier (ACC), a software tool capable of identifying cellular phenotypes based on features extracted from the image. It provides an interface for a user to efficiently train machine learning methods to predict various phenotypes. We developed the Suggest a Learner (SALT) toolbox, which selects the optimal machine learning algorithm and parameters for a particular classification problem. For cases where discrete cell-based decisions are not suitable, we propose a method to use multi-parametric regression to analyze continuous biological phenomena. Finally, to improve the learning speed and accuracy, we recently developed an active learning scheme which automatically selects the most informative cell samples. |
关 键 词: | 像素之外的生命; 数据科学; HCS的机器学习和图像分析方法; 多参数回归分析; 高级细胞分类器 |
课程来源: | 视频讲座网 |
数据采集: | 2022-10-14:cyh |
最后编审: | 2022-11-29:liyy |
阅读次数: | 45 |