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Titel:

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

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Vortrag / Präsentation
Autor(en):
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.
Stichworte:
Blood Cell Analysis, Autoencoder, Convolutional Neural Networks, Digital Holographic Microscopy, Phase Images
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Herausgeber:
Roberto Moreno-Díaz, Franz R. Pichler, Alexis Quesada-Arencibia
Kongress- / Buchtitel:
Computer Aided Systems Theory - EUROCAST 2019
Verlag / Institution:
Springer
Jahr:
2019
Jahr / Monat:
2019-02
Monat:
Feb
Seiten:
8
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