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  Using Deep Correlation Features to define the Meta Style of Cell Images for Classification

Grützmacher, S., Kemkemer, R., & Curio, C. (2019). Using Deep Correlation Features to define the Meta Style of Cell Images for Classification. Current Directions in Biomedical Engineering, 5(1), 227-230. doi:10.1515/cdbme-2019-0058.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-BFF1-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-BFF2-4
Genre: Journal Article

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CurrDirektBiomedEngin_5_2019_227.pdf (Any fulltext), 661KB
 
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 Creators:
Grützmacher, Simon, Author
Kemkemer, Ralf1, Author              
Curio, Cristóbal, Author
Affiliations:
1Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society, ou_2364731              

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Free keywords: Machine Learning; Cell Imaging; Structural Images
 Abstract: 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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Language(s): eng - English
 Dates: 2019-09-182019-09-01
 Publication Status: Published in print
 Pages: 4
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1515/cdbme-2019-0058
 Degree: -

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Title: Current Directions in Biomedical Engineering
Source Genre: Journal
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Publ. Info: Berlin : De Gruyter
Pages: - Volume / Issue: 5 (1) Sequence Number: - Start / End Page: 227 - 230 Identifier: ISSN: 2364-5504
CoNE: https://pure.mpg.de/cone/journals/resource/2364-5504