TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Grützmacher, Simon
A1  - Kemkemer, Ralf
A1  - Curio, Cristóbal
T1  - Using deep correlation features to define the meta style of cell images for classification
JF  - Current directions in biomedical engineering
N2  - 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.
KW  - machine learning
KW  - cell imaging
KW  - structural images
Y1  - 2019
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-23999
SN  - 2364-5504
SS  - 2364-5504
U6  - https://doi.org/10.1515/cdbme-2019-0058
DO  - https://doi.org/10.1515/cdbme-2019-0058
VL  - 5
IS  - 1
SP  - 227
EP  - 230
S1  - 4
PB  - De Gruyter
CY  - Berlin
ER  -