Life beyond the pixels: machine learning and image analysis methods for HCS

author: Peter Horvath, Hungarian Academy of Sciences
published: June 28, 2019,   recorded: May 2019,   views: 52
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Description

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) (www.cellclassifier.org), 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.

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Download slides icon Download slides: icgeb_horvath_image_analysis_methods_01.pdf (13.4┬áMB)


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