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Document type:
Konferenzbeitrag 
Contribution type:
Vortrag / Präsentation 
Author(s):
Stefan Röhrl, Matthias Ugele, Christian Klenk, Dominik Heim, Oliver Hayden and Klaus Diepold 
Title:
Autoencoder Features for Differentiation of Leukocytes based on Digital Holographic Microscopy (DHM) 
Abstract:
The differentiation and counting of leukocytes is essential for the diagnosis of leukemia. This work investigates the suitability of Deep Convolutional Autoencoders and Principal Component Analysis (PCA) to generate robust features from the 3D image data of a digital holographic microscope (DHM). The results show that the feature space is not trivially separable in both cases. A terminal classification by a Support Vector Machine (SVM) favors the uncorrelated PCA features. 
Keywords:
Blood Cell Analysis, Autoencoder, Convolutional Neural Networks, Digital Holographic Microscopy, Phase Images 
Dewey Decimal Classification:
620 Ingenieurwissenschaften 
Editor:
Roberto Moreno-Díaz, Franz R. Pichler, Alexis Quesada-Arencibia 
Book / Congress title:
Computer Aided Systems Theory - EUROCAST 2019 
Publisher:
Springer 
Year:
2019 
Year / month:
2019-02 
Month:
Feb 
Pages: