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Title:

Autoencoder Features for Differentiation of Leukocytes based on Digital Holographic Microscopy (DHM)

Document type:
Konferenzbeitrag
Contribution type:
Vortrag / Präsentation
Author(s):
Stefan Röhrl, Matthias Ugele, Christian Klenk, Dominik Heim, Oliver Hayden and Klaus Diepold
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:
8
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