Publication:
Deep learning for scanning electron microscopy: Synthetic data for the nanoparticles detection

dc.contributor.authorKharin, A. Y.
dc.date.accessioned2024-11-27T08:15:25Z
dc.date.available2024-11-27T08:15:25Z
dc.date.issued2020
dc.description.abstract© 2020 Elsevier B.V.Deep learning algorithms are one of most rapid developing fields into the modern computation technologies. One of the bottlenecks into the implementation of such advaced algorithms is their requirement for a large amount of manually-labelled data for training. For the general-purpose tasks, such as general purpose image classification/detection the huge images datasets are already labelled and collected. For more subject specific tasks (such as electron microscopy images treatment), no labelled data available. Here I demonstrate that a deep learning network can be successfully trained for nanoparticles detection using semi-synthetic data. The real SEM images were used as a textures for rendered nanoparticles at the surface. Training of RetinaNet architecture using transfer learning can be helpful for the large-scale particle distribution analysis. Beyond such applications, the presented approach might be applicable to other tasks, such as image segmentation.
dc.identifier.citationKharin, A. Y. Deep learning for scanning electron microscopy: Synthetic data for the nanoparticles detection / Kharin, A.Y. // Ultramicroscopy. - 2020. - 219. - 10.1016/j.ultramic.2020.113125
dc.identifier.doi10.1016/j.ultramic.2020.113125
dc.identifier.urihttps://www.doi.org/10.1016/j.ultramic.2020.113125
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85091965986&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000594768500003
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/22405
dc.relation.ispartofUltramicroscopy
dc.titleDeep learning for scanning electron microscopy: Synthetic data for the nanoparticles detection
dc.typeArticle
dspace.entity.typePublication
oaire.citation.volume219
relation.isOrgUnitOfPublicationc8407a6f-7272-450d-8d99-032352c76b55
relation.isOrgUnitOfPublication.latestForDiscoveryc8407a6f-7272-450d-8d99-032352c76b55
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