Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection

ABSTRACT The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed “mimicry embedding,” for rapid application of neural network arch...

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Autores principales:	Artur Yakimovich, Moona Huttunen, Jerzy Samolej, Barbara Clough, Nagisa Yoshida, Serge Mostowy, Eva-Maria Frickel, Jason Mercer
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	capsule networks transfer learning superresolution microscopy vaccinia virus Toxoplasma gondii zebrafish Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/d26b0508dfde4b1581e6233c5ef9daa5
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id	oai:doaj.org-article:d26b0508dfde4b1581e6233c5ef9daa5
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spelling	oai:doaj.org-article:d26b0508dfde4b1581e6233c5ef9daa52021-11-15T15:30:58ZMimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection10.1128/mSphere.00836-202379-5042https://doaj.org/article/d26b0508dfde4b1581e6233c5ef9daa52020-10-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSphere.00836-20https://doaj.org/toc/2379-5042ABSTRACT The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed “mimicry embedding,” for rapid application of neural network architecture-based analysis of pathogen imaging data sets. Embedding of a novel host-pathogen data set, such that it mimics a verified data set, enables efficient deep learning using high expressive capacity architectures and seamless architecture switching. We applied this strategy across various microbiological phenotypes, from superresolved viruses to in vitro and in vivo parasitic infections. We demonstrate that mimicry embedding enables efficient and accurate analysis of two- and three-dimensional microscopy data sets. The results suggest that transfer learning from pretrained network data may be a powerful general strategy for analysis of heterogeneous pathogen fluorescence imaging data sets. IMPORTANCE In biology, the use of deep neural networks (DNNs) for analysis of pathogen infection is hampered by a lack of large verified data sets needed for rapid network evolution. Artificial neural networks detect handwritten digits with high precision thanks to large data sets, such as MNIST, that allow nearly unlimited training. Here, we developed a novel strategy we call mimicry embedding, which allows artificial intelligence (AI)-based analysis of variable pathogen-host data sets. We show that deep learning can be used to detect and classify single pathogens based on small differences.Artur YakimovichMoona HuttunenJerzy SamolejBarbara CloughNagisa YoshidaSerge MostowyEva-Maria FrickelJason MercerAmerican Society for Microbiologyarticlecapsule networkstransfer learningsuperresolution microscopyvaccinia virusToxoplasma gondiizebrafishMicrobiologyQR1-502ENmSphere, Vol 5, Iss 5 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	capsule networks transfer learning superresolution microscopy vaccinia virus Toxoplasma gondii zebrafish Microbiology QR1-502
spellingShingle	capsule networks transfer learning superresolution microscopy vaccinia virus Toxoplasma gondii zebrafish Microbiology QR1-502 Artur Yakimovich Moona Huttunen Jerzy Samolej Barbara Clough Nagisa Yoshida Serge Mostowy Eva-Maria Frickel Jason Mercer Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection
description	ABSTRACT The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed “mimicry embedding,” for rapid application of neural network architecture-based analysis of pathogen imaging data sets. Embedding of a novel host-pathogen data set, such that it mimics a verified data set, enables efficient deep learning using high expressive capacity architectures and seamless architecture switching. We applied this strategy across various microbiological phenotypes, from superresolved viruses to in vitro and in vivo parasitic infections. We demonstrate that mimicry embedding enables efficient and accurate analysis of two- and three-dimensional microscopy data sets. The results suggest that transfer learning from pretrained network data may be a powerful general strategy for analysis of heterogeneous pathogen fluorescence imaging data sets. IMPORTANCE In biology, the use of deep neural networks (DNNs) for analysis of pathogen infection is hampered by a lack of large verified data sets needed for rapid network evolution. Artificial neural networks detect handwritten digits with high precision thanks to large data sets, such as MNIST, that allow nearly unlimited training. Here, we developed a novel strategy we call mimicry embedding, which allows artificial intelligence (AI)-based analysis of variable pathogen-host data sets. We show that deep learning can be used to detect and classify single pathogens based on small differences.
format	article
author	Artur Yakimovich Moona Huttunen Jerzy Samolej Barbara Clough Nagisa Yoshida Serge Mostowy Eva-Maria Frickel Jason Mercer
author_facet	Artur Yakimovich Moona Huttunen Jerzy Samolej Barbara Clough Nagisa Yoshida Serge Mostowy Eva-Maria Frickel Jason Mercer
author_sort	Artur Yakimovich
title	Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection
title_short	Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection
title_full	Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection
title_fullStr	Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection
title_full_unstemmed	Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection
title_sort	mimicry embedding facilitates advanced neural network training for image-based pathogen detection
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/d26b0508dfde4b1581e6233c5ef9daa5
work_keys_str_mv	AT arturyakimovich mimicryembeddingfacilitatesadvancedneuralnetworktrainingforimagebasedpathogendetection AT moonahuttunen mimicryembeddingfacilitatesadvancedneuralnetworktrainingforimagebasedpathogendetection AT jerzysamolej mimicryembeddingfacilitatesadvancedneuralnetworktrainingforimagebasedpathogendetection AT barbaraclough mimicryembeddingfacilitatesadvancedneuralnetworktrainingforimagebasedpathogendetection AT nagisayoshida mimicryembeddingfacilitatesadvancedneuralnetworktrainingforimagebasedpathogendetection AT sergemostowy mimicryembeddingfacilitatesadvancedneuralnetworktrainingforimagebasedpathogendetection AT evamariafrickel mimicryembeddingfacilitatesadvancedneuralnetworktrainingforimagebasedpathogendetection AT jasonmercer mimicryembeddingfacilitatesadvancedneuralnetworktrainingforimagebasedpathogendetection
_version_	1718427915101667328

Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection

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