Publication type: Article in scientific journal
Type of review: Not specified
Title: Single-cell phenotype classification using deep convolutional neural networks
Authors : Dürr, Oliver
Sick, Beate
DOI : 10.1177/1087057116631284
Published in : Journal of Biomolecular Screening
Volume(Issue) : 21
Issue : 9
Pages : 998
Pages to: 1003
Issue Date: 2016
Publisher / Ed. Institution : Sage
ISSN: 1087-0571
1552-454X
Language : English
Subjects : Cell-based assays; Deep learning; High-content screening; Single-cell classification; Algorithms; Computer-assisted image processing; Machine learning; Single-cell analysis; Support vector machine; Neural networks (computer)
Subject (DDC) : 005: Computer programming, programs and data
Abstract: Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.
Further description : Published online: 12 February 2016
URI: https://digitalcollection.zhaw.ch/handle/11475/13399
Fulltext version : Published version
License (according to publishing contract) : Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in Collections:Publikationen School of Engineering

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