Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

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


Kemkemer,  Ralf
Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

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.

Cite as: https://hdl.handle.net/21.11116/0000-0004-BFF1-5
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.