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Using deep correlation features to define the meta style of cell images for classification

  • Digital light microscopy techniques are among the most widely used methods in cell biology and medical research. Despite that, the automated classification of objects such as cells or specific parts of tissues in images is difficult. We present an approach to classify confluent cell layers in microscopy images by learned deep correlation features using deep neural networks. These deep correlation features are generated through the use of gram-based correlation features and are input to a neural network for learning the correlation between them. In this work we wanted to prove if a representation of cell data based on this is suitable for its classification as has been done for artworks with respect to their artistic period. The method generates images that contain recognizable characteristics of a specific cell type, for example, the average size and the ordered pattern.

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Metadaten
Name:Grützmacher, Simon; Kemkemer, Ralf; Curio, Cristóbal
URN:urn:nbn:de:bsz:rt2-opus4-23999
DOI:https://doi.org/10.1515/cdbme-2019-0058
ISSN:2364-5504
Erschienen in:Current directions in biomedical engineering
Publisher:De Gruyter
Place of publication:Berlin
Document Type:Article
Language:English
Year of Publication:2019
Tag:cell imaging; machine learning; structural images
Volume:5
Issue:1
Pagenumber:4
First Page:227
Last Page:230
Dewey Decimal Classification:570 Biowissenschaften, Biologie
Open Access:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International