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Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. (Spjuth group)ORCID-id: 0000-0002-5295-010X
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. (Spjuth group)ORCID-id: 0000-0003-4046-9017
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.ORCID-id: 0000-0002-8083-2864
2019 (engelsk)Inngår i: SLAS discovery : advancing life sciences R & D, ISSN 2472-5552, Vol. 24, nr 4, s. 466-475Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The quantification and identification of cellular phenotypes from high-content microscopy images has proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis to quantify changes in cell morphology, which requires several nontrivial and independent analysis steps. Recently, convolutional neural networks have emerged as a compelling alternative, offering good predictive performance and the possibility to replace traditional workflows with a single network architecture. In this study, we applied the pretrained deep convolutional neural networks ResNet50, InceptionV3, and InceptionResnetV2 to predict cell mechanisms of action in response to chemical perturbations for two cell profiling datasets from the Broad Bioimage Benchmark Collection. These networks were pretrained on ImageNet, enabling much quicker model training. We obtain higher predictive accuracy than previously reported, between 95% and 97%. The ability to quickly and accurately distinguish between different cell morphologies from a scarce amount of labeled data illustrates the combined benefit of transfer learning and deep convolutional neural networks for interrogating cell-based images.

sted, utgiver, år, opplag, sider
2019. Vol. 24, nr 4, s. 466-475
Emneord [en]
cell phenotypes, deep learning, high-content imaging, machine learning, transfer learning
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Identifikatorer
URN: urn:nbn:se:uu:diva-375566DOI: 10.1177/2472555218818756ISI: 000461840200004PubMedID: 30641024OAI: oai:DiVA.org:uu-375566DiVA, id: diva2:1284197
Forskningsfinansiär
Swedish Foundation for Strategic Research Swedish National Infrastructure for Computing (SNIC)Tilgjengelig fra: 2019-01-31 Laget: 2019-01-31 Sist oppdatert: 2019-05-06bibliografisk kontrollert

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