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dc.contributor.authorDürr, Oliver-
dc.contributor.authorSick, Beate-
dc.date.accessioned2018-11-30T09:39:57Z-
dc.date.available2018-11-30T09:39:57Z-
dc.date.issued2016-
dc.identifier.issn1087-0571de_CH
dc.identifier.issn1552-454Xde_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13399-
dc.descriptionPublished online: 12 February 2016de_CH
dc.description.abstractDeep 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%.de_CH
dc.language.isoende_CH
dc.publisherSagede_CH
dc.relation.ispartofJournal of Biomolecular Screeningde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectCell-based assaysde_CH
dc.subjectDeep learningde_CH
dc.subjectHigh-content screeningde_CH
dc.subjectSingle-cell classificationde_CH
dc.subjectAlgorithmsde_CH
dc.subjectComputer-assisted image processingde_CH
dc.subjectMachine learningde_CH
dc.subjectSingle-cell analysisde_CH
dc.subjectSupport vector machinede_CH
dc.subjectNeural networks (computer)de_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleSingle-cell phenotype classification using deep convolutional neural networksde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1177/1087057116631284de_CH
dc.identifier.pmid26950929de_CH
zhaw.funding.euNode_CH
zhaw.issue9de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1003de_CH
zhaw.pages.start998de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume21de_CH
zhaw.publication.reviewNot specifiedde_CH
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Dürr, O., & Sick, B. (2016). Single-cell phenotype classification using deep convolutional neural networks. Journal of Biomolecular Screening, 21(9), 998–1003. https://doi.org/10.1177/1087057116631284
Dürr, O. and Sick, B. (2016) ‘Single-cell phenotype classification using deep convolutional neural networks’, Journal of Biomolecular Screening, 21(9), pp. 998–1003. Available at: https://doi.org/10.1177/1087057116631284.
O. Dürr and B. Sick, “Single-cell phenotype classification using deep convolutional neural networks,” Journal of Biomolecular Screening, vol. 21, no. 9, pp. 998–1003, 2016, doi: 10.1177/1087057116631284.
DÜRR, Oliver und Beate SICK, 2016. Single-cell phenotype classification using deep convolutional neural networks. Journal of Biomolecular Screening. 2016. Bd. 21, Nr. 9, S. 998–1003. DOI 10.1177/1087057116631284
Dürr, Oliver, and Beate Sick. 2016. “Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.” Journal of Biomolecular Screening 21 (9): 998–1003. https://doi.org/10.1177/1087057116631284.
Dürr, Oliver, and Beate Sick. “Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.” Journal of Biomolecular Screening, vol. 21, no. 9, 2016, pp. 998–1003, https://doi.org/10.1177/1087057116631284.


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